provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2020.jsonl.gz.gz

text:
Accelerated ageing as a vigour test on chickpea seeds

The objective of this work was to establish the temperature and the period of exposure needed to perform the traditional accelerated ageing test with saturated NaCl salt solution on chickpea seeds. Eight seed lots from two cultivars, including four seeds each, were subjected to germination and vigour tests for initial characterization. Following the methodology of the traditional accelerated ageing test. The seeds were placed on stainless steel screens in boxes with 40 mL deionized water. They were kept at 41 and 45o Celsius for 24 and 48 hours. Furthermore, the accelerated ageing test using [40 g of NaCl in 100 mL of water, thus providing 76% of relative humidity in air] NaCl (salt) solution in place of the deionized water was performed. Aged seeds were sowed in germination paper and kept in a germination chamber at 41o and 45o Celsius. The evaluations were performed five and eight days after sowing. The statistical analysis was performed with a completely randomized design and four replications. The traditional ageing of both cultivars showed a water content higher than the tolerated limits at 48 hour period and 41 and 45 °C. In the saturated saline test, the cultivar BRS Aleppo produced consistent results at temperatures 45 °C for 24 h. The cultivar Cícero showed a low correlation with the initial characterization as the results of accelerated ageing. Thus, for ‘BRS Aleppo’, the recommended accelerated ageing methodology was application of saturated saline solution, when seed exposed to 41 °C for 24 hours with germination evaluation at the first count date. For 'Cicero', satisfactory results were not obtained, and new studies are required to establish a methodology.

Introduction
Chickpea (Cicer arietinum L.) is a highly nutritious legume and an excellent source of protein, carbohydrates, minerals, vitamins, and fiber . It also has better digestibility than other grains and a low content of antinutritional substances. It is estimated that 11.6 million tons of this grain are produced annually in the world, especially in developing countries (ICRISAT, 2018). In Brazil there has been an increase in the consumption of chickpeas over the last 10 years, mainly due to the high protein content. According to Paula Júnior and Venzon (2007), the main form of consumption in Brazil is as whole grains cooked and served in the form of salad or pasta. According to Hoskem et al. (2017), despite being a cold climate plant, the chickpea adapts well to tropical regions like Brazil, producing crops with good development and high productivity.
World production of chickpeas has only marginally increased over the last three decades due to the decline in planted area, while productivity has increased by around 37% over the same period. The increasing productivity of this legume is due to improvements in the cultivation, which includes research on the material used in planting, as well as cultivation techniques (Nascimento et al., 2016). The choice of high quality seeds directly influences the satisfactory establishment of the plants in the field and, consequently, high productivity indexes. One of the aspects that interferes with the productivity of the crops is the of plants in the field. Among many factors, population of depends depends on the quality of the seeds. In order to establish storage, commercialization, and quality control policies, seed companies have used the results of vigour tests in conjunction with germination tests for decision making, especially in the comparison of lots (Martins et al., 2014). Among the tests that evaluate the physiological quality of the seeds, we have the accelerated ageing test, which is a commonly used test of vigour. It has been successfully used for soybean seeds (Marcos-Filho, 1999;Dutra and Vieira, 2004), corn (Fessel et al., 2006), and other species such as crambe (Leão et al., 2016). In this test, the seeds are subjected to high temperature and relative humidity for a relatively short period and then placed to germinate. Lots of high-vigour seeds will remain viable after being submitted to these conditions, while those of low vigour will have their viability reduced (Nascimento et al., 2014;Marcos-Filho, 2015). Thus, the temperature used and the time of exposure to this temperature are factors that influence the quality of the results generated by the test. The use of saturated salt solution in alternative acceleration ageing methodologies aims to reduce the relative humidity of the air during the exposure of the seeds to high temperatures. The effect of this reduction is the slower rate of water uptake and consequently a reduction in seed deterioration (Jianhua and McDonald, 1996;Panobianco and Marcos-Filho, 2001). This alternative has been recommended for evaluation of several species such as tomato (Panobianco and Marcos-Filho, 2001), coriander (Radke et al., 2016), and rice (Monteiro et al., 2017). The objective of this study was to establish an efficient methodology to perform the traditional accelerated ageing test (with water) and alternative test with saturated NaCl salt solution (76% relative humidity in air) to determine the optimal period and temperature of exposure for chickpea seeds. Table 1 shows the data obtained in the initial physiological characterization tests. For 'BRS Aleppo', the germination first count (FC) ranked the lots in four levels, as the most sensitive test to detect differences in the vigour of this cultivar. The FC was also efficient to identify differences in the vigour of lots of okra seeds, as reported by Torres et al. (2014), which compared FC with germination and seedling emergence tests. The FC test is recommended for evaluating the seed vigour of several species such as Cucumis melo L. (Bhering et al., 2004) and Beta vulgaris L. (Silva and Vieira, 2006). The efficiency of the test can be explained, according to Marcos-Filho (2015), by the fact that one of the first expressions of the process of seed deterioration is the reduction of the germination process speed. In the G, SE and ESI tests, there was a distinction in three levels of vigour, in which lot 1 was classified as the best and lot 3 the worst. In the SL-RS, lots were classified into two levels. Finally, the MET and SL-AP and SL-T tests did not rank the vigour levels of seed lots and were not efficient in classifying them. In general, after initial characterization, lot 1 had the most vigour, followed by lots 4 and 2 as intermediates and lot 3 with the worst performance. In the case of the Cícero cultivar, the ESI was the test that efficiently classified the lots, resulting in three levels. As with FC, the ESI test evaluates the speed of the germination process, which is strongly affected by the physiological state of the seeds, i.e. deteriorating seeds will take more time to complete germination process tasks (Marcos-Filho, 2015). In Sorghum bicolor L. Moench Ullmann et al. (2015), used ESI as a method to evaluate the effect of temperatures during the drying of seeds, observing satisfactory results with the use of this test. The FC, G, SE and SL tests ranked the lots within two levels, with lot 2 having higher vigour (Table 1). The MET test did not rank the lots at vigour levels. Therefore, according to the tests, lot 2 presented better results, while lots 1 and 3 had worse performance.

Initial seed moisture content and after the variations of AA tests
The seed moisture content of the seeds directly influenced several aspects of its physiological quality. Therefore, its determination is fundamental in quality tests of seed lots (Sarmento et al., 2015). Thus, the determination of the initial seed moisture content and moisture content after the ageing of the seeds guarantees the reliability of the vigour tests. The initial seed moisture content should not vary by more than 2.0 percentage points between lots (Marcos-Filho, 2015), which was the variation obtained in this experiment (Table 2) for both cultivars. After the ageing of the seeds, the same variation is expected according to Marcos-Filho (2015). In our study, this variation was exceeded at 45 °C for 48 hours of exposure for both cultivars, which makes it difficult to recommend these variables. For this same period, the variation was slightly higher than 2.0 percentage points at 41 °C. The higher values of MC were observed at 45° C compared t 41° C. This finding can be explained by the fact that, at higher temperatures, the kinetic energy of the system is high so there is greater absorption of water by the seeds (Dias and Marcos-Filho, 1995). The MC obtained after AASS were lower than those found with AA (Table 3). Using the methodology with saturated saline solution, the relative humidity is reduced from 100% to 76% (Jianhua and McDonald, 1996). Thus, the deterioration rates of seeds are reduced compared to the AA test, due to the lower water absorption. This test could simulate moisture conditions during seed storage (Peñaloza et al., 2005). In AASS the seed moisture content after ageing test met the maximum variation of 2.0 percentage points suggested by Marcos-Filho (2015), ensuring consistency of results.

Thousand seeds weight
The TSW test revealed that the seeds of the cultivar Cícero are substantially larger than the seeds of the BRS Aleppo cultivar (Table 4). The seeds of the lot 1 of the Cícero cultivar presented TSW approximately twice as large as seeds of the lot 2 of 'BRS Aleppo'. The characteristics of the cultivars are the results of the crosses and/or selections made by breeding programs. The size of the seeds may vary between cultivars and influence in several aspects such as the physiological quality of the seeds.

Accelerated ageing -traditional and with saturated saline solution
The cultivar BRS Aleppo showed the best overall performance of accelerated ageing (AA), considering the temperatures of 41 and 45 ºC for the periods of 24 h and 48 Table 1. Initial characterization of seed lots of chickpea seeds by germination first count (FC), germination (G), seedlings emergence on field (SE), speed emergence index (ESI), average time of emergence (MET), seedling length (SL) -aerial part (SL-AP), root system (SL-RS) and total (SL-T     Leão et al. (2016) reported that crambe seeds should be exposed to 41 ºC for 48 h in the AA test. For Cícero cultivar, Lot 2 showed the best performance, except for FC at 41 ºC for 24 h. Lots 3 and 4 were generally classified as intermediary performances, with some differences in rank. Finally, lot 1 presented the worst performance compared to others. In most cases, the lots distinction was occurred in only two vigour levels in FC, except for 41° C and 45 °C for 48 h. On the other hand, the FC at 45 ºC for 48 h showed three levels of vigour ranking of the lots. This classification with some small differences is similar with that initial characterization. Similar results were found by Silva and Cícero (2014) with tomato seeds, in which the initial characterization tests classified the lots differently from the classification obtained in accelerated ageing tests. This can be explained by the definition of vigour. According to ISTA (2014): a set of properties will determine the performance of lots under wide range of environmental conditions. Therefore, using a single test or tests that are based on the same principles that affect seed performance cannot generate complete information about seed quality, according to Hampton and Coolbear (1990). The germination of the seeds submitted to the AASS presented the same tendency obtained for AA, differing in some points. For the cultivar BRS Aleppo, lot 1 showed better performance in all cases, lot 4 soon followed in intermediate position along with lot 2, and finally lot 3, which under all conditions had the worst performance (  (Table 4) may be responsible for the low efficiency of the accelerated ageing tests to detect the vigour of the lots. Jianhua and McDonald (1996) reported that seed size can interfere with accelerated ageing. Larger seeds may exhibit variations in water absorption during ageing, as well as variations in the effect of temperature along the whole seed length, influencing the level of deterioration reached by the seeds of the sample during the test. Consequently, it also affects the efficiency of these tests in ranking seed lots for vigor levels.

Variables association
Pearson correlation (Cor) and coincidence index of the means (CIM) results are available in Table 7. For 'BRS Aleppo' CIM values were high, mainly in AASS. Using as a criterion, the highest values for Cor and CIM was observed for the AA test, which is best correlated with SE when performed at 41 °C for 24 h in LC and for 48 h in FC and LC. One of the requirements of the vigour tests is that they are fast. For agile decision making by the seed industries, the variable 41 °C for 48 h and evaluation in the FC would be the best alternative for the AA test. However, changes in seed moisture content, before and after exposure to AA, greater than 2 percentage points (Table 2) precluded the recommendation of this test. Similar results were obtained by Gordin et al. (2015), which did not recommend the use of the traditional method in Guizotia abyssinica seeds because it provides variation of the seed moisture content between the samples greater than the tolerable standard.
For the AASS test, the temperature of 45 °C for 24 h and evaluation in both FC and LC were the most consistent. Following the criterion of agility, the best option is the evaluation in the FC. These results agree with those obtained by Leão et al. (2016) studying the AASS test for crambe seeds. The authors concluded that 24 h at 41 or 45 °C is best to determine vigour in this species. These authors also agreed that post-ageing assessment is best performed on the date of the first count.
The results for the cultivar Cicero did not present the same tendency, as the values of CIM were low. In addition, results were found to be null or negative for Cor, due to the fact that there was no relation between the SE test in the initial characterization and the AA. This may also be due to the effect of seed size on the efficiency of ageing procedures, as discussed earlier. Amaro et al. (2014), used the Pearson correlation methodology in crambe seeds. They also reported the absence of correlation between combinations of the accelerated ageing test and those of the other initial characterization tests that had been useful in the stratification of lots for vigour and initial quality assessment tests.

Plant material
Four lots of chickpea (Cicer arietinum L.) of BRS Aleppo cultivar and four of cultivar Cícero were used. Before the beginning of the experiments the seeds were stored in a 'kraft' paper bag in a cold room (16 °C and 50% of relative humidity in air) for one month. The research was conducted at the Federal Institute Goiano, Campus Urutaí, Laboratory of Seed Analysis.

Initial characterization of the seed lots
Routine tests for physical and physiological characterization of seeds were initially developed to allow comparison with the variables of accelerated aging tests. For the initial characterization of the lots, the tests described below were conducted: Seed moisture content (MC): determined by the oven-drying method at 105 ± 3 ºC for 24 h (Brasil, 2009), using two subsamples of approximately 10.0 g of seeds for each lot. The results were expressed as percentage (wet basis). It was carried out initially to characterize the lots and after each variable of the tests of traditional accelerated ageing and with saturated saline solution.
Thousand seeds weight (TSW): the mass of eight replicates of 100 seeds of each lot were obtained (Brasil, 2009). The variance, standard deviation and coefficient of variation were calculated for each lot. With coefficient of variation values lower than 4%, the mean weight was multiplied by 10 and the results were expressed in grams. Germination (G): for each lot, four replicates of 50 seeds were randomly assigned to the portion of pure seeds, arranged under two sheets of germinating paper and another sheet over the seeds, and then rolled up. The papers were previously moistened with water the equivalent of 3.0 times the weight of the dry substrate. The rolls were conditioned in a germinator at constant temperature of 20 °C. After the five-day period the data of germination first count (FC) were obtained, and eight days after sowing, data of final germination (G) were obtained (Brasil, 2009). The results were expressed as a percentage of normal seedlings. Seedlings Length (SL): four replicates of 10 seeds, in longitudinal direction, were placed in the upper 1/3 of the germination paper. The papers were moistened as described for the germination test. The seeds were positioned so that the radicle region was facing the bottom of the paper. The rolls were kept under the same conditions mentioned for the germination test. Eight days after sowing, measurements of the emergent normal seedlings (aerial part [AP] and root system [RS]) were carried out using a digital caliper. The sum of SL-AP and SL-RS was called SL-T, which revealed the average total length of normal seedlings. The results were expressed in centimeters. Seedlings emergence on field (SE): a sprinkler irrigation system was used on the field. Four replications of 50 seeds per lot were sown so that the seeds remained at 3.0 cm depth, arranged in a soil bed without fertilization in lines of 2.5 m of length and spaced 0.5 m. A hypocotyl greater than 1 cm in length at the time of the evaluation (21 days) was adopted as an evaluation criterion for determining the percentage of emerged normal seedlings. Emergence speed index (ESI): calculated at the end of the SE test, using the formula proposed by Maguire (1962). Performed together with the SE test with seedling counting every 72 h until the 21st day, considering the number of seedlings that presented the cotyledons above the soil surface.
Mean emergence time (MET): values were expressed in days and obtained from the formula of Labouriau (1983), with the same data obtained for ESI.

Adaptation of the accelerated aging test methodologytraditional and saturated saline solution
To adapt the accelerated ageing test methodology, as a method to evaluate the vigour of chickpea seeds, the lots were submitted to the following tests: Accelerated ageing (AA): the seeds were distributed in a single layer on the stainless-steel screen, placed in a Gerbox type box with 40 mL deionized water at the bottom and kept in an ageing chamber. The seed boxes remained at 41 and 45 ºC for 24 and 48 h. After these periods, four replicates of 50 seeds were used for the germination test as described previously. The evaluations were performed at five (FC) and eight days (LC) after sowing and the results expressed as a percentage of normal seedlings. Accelerated ageing with saturated saline solution (AASS): The same procedure described for conducting the traditional procedure was used, except that the 40 mL volume of deionized water was replaced with NaCl salt solution (Jianhua and McDonald, 1996). This solution was obtained by diluting 40 g of NaCl in 100 mL of water, thus providing 76% of relative humidity in air. Evaluations were also performed with five (FC) and eight days (LC) and the results expressed as a percentage of normal seedlings.

Statistical analysis
The initial physiological characterization tests were installed separately for each cultivar and the analysis performed individually for each test (FC, G, SL, SE, ESI and MET). They were installed in a completely randomized design with four replicates. The residues were tested with the Shapiro-Wilk (for normality) and Bartlett tests (homoscedasticity). The data were submitted to analysis of variance by the F-test. For comparison of the means, the LSD test was used, at 10% probability.
In the interest of the accuracy of the AA and AASS methodologies, the tests were set up separately for each cultivar and the analysis performed individually for each variation of the methodology: temperature, exposure period and period to evaluate. The residues were tested with the Shapiro-Wilk (for normality) and Bartlett tests (homoscedasticity). The data were submitted to analysis of variance by the F-test. For comparison of the means, the LSD test was used at 10% probability.
To compare the initial characterization more precisely, the test of SE with each methodological variation of the AA, and tests of association of the variables were performed. The test for association for the variables was analyzed using the Pearson correlation (Cor) and the coincidence index of the means tests (CIM) in percentage, between the variables of AA or AASS tests and SE. This percentage was calculated as follows: 0% was used when no letter was matched between AA or AASS and SE tests and 100% when all letters of the mean tests were the same, comparing AA or AASS with SE. All analyzes were performed using the R software (R Core Team, 2014).

Conclusions
For BRS Aleppo cultivar, the recommended accelerated ageing methodology to evaluate seed vigour is the use of saturated saline solution, with the seed exposure for 24 h at 41 °C and germination evaluation at the first count data, at five days after sowing. As for Cicero cultivar, satisfactory results were not obtained, and new studies are required in order to establish a methodology.


extfieldsofstudy: Biology Physics

text:
Evaluation of hybrid corn genotype on productivity improvement to support national food availability

Potential lines need to be tested to produce new superior varieties of hybrids with high yield potential to increase corn productivity. The purpose of the research is to produce a single cross-strain has the appearance of a good agronomist, double cob and high yield potential, as a candidate release of varieties. The study began in April to July 2018. Experimental site in the Lembar village, West Lombok Regency, West Nusa Tenggara Province. Eight crosses of a single cross-tested all showed the appearance of a good agronomist, in accordance with the criteria of belonging to four varieties comparison (Bisi-2, Nasa-29, HJ-21 and Bima-3). There is one the crosses cross-E2 x Mr14 was good combiner for double ear with the percentage exceeded 28% per ha. There are four single cross-E 2 X Mr 14, E 1 X Mr 14, E 65 X Mr 14, MS 3 X Mr 14, which has a high yield potential with the weight of the dried seeds > 13 ton/ha. There are four potential lines that can be released as a new high yielding varieties.

Introduction
Food is a basic need for people to be able to maintain life. Therefore, the adequacy of food for every person at all times is a human right that deserves to be fulfilled. Based on this fact, the problem of fulfilling food needs for all residents at any time in a region is the main target of food policy for a country's government [1].
In Indonesia, corn is one of the foods that has the potential to be developed as local food [2]. While based on the order of the world's basic food ingredients, corn ranks third after wheat and rice. Besides being a food ingredient, corn is also used for feed and industrial raw materials. National maize needs in each year increased significantly along with population growth [3].
Corn occupies an important position in the economy as well as national food security because of its extensive use as a source of food, animal feed and industrial raw materials. Its extensive use and high demand for corn make this plant one of the four strategic food commodities in addition to rice, soybeans, and cassava which receive special attention from the ASEAN economic community (MEA) [4].
Although corn production in Indonesia is the highest compared to other ASEAN countries which reached 18.5 million tons in 2013, national maize needs reached 20.8 million tons [5]. Thus, efforts to  2 improve national corn productivity in an integrated and sustainable manner are a necessity so that food security and national food sovereignty can be achieved.
Efforts to increase corn productivity, one of which can be done by providing varieties of corn varieties that have high yield potential, namely hybrid varieties. Hybrid varieties have high yield potential, 15-20% higher than free-range varieties in addition to providing a high uniformity of agronomic appearance and early harvesting age [6]. Hybrid varieties also show better plant performance in stressed environmental conditions, including acid soils [7], [8]; [9]. Superior hybrids are not only determined by the appearance of parent inbred lines, but are also determined by the ability to join in inbred lines crossed to produce hybrids [4].
This study aims to evaluate several genotypes of single-cross hybrid maize to obtain hybrids that have good agronomic appearance, have the potential for double ear and have high yield. It is expected that from the various hybrids evaluated, new hybrid candidates will be obtained with higher yields than commercial hybrids.

Materials and Methods
The research was conducted in Lembar Village, Lembar District, West Lombok Regency, West Nusa Tenggara from April to July 2018. Research Sites in rice field agroecosystems, which are at an altitude of 30 m above sea level, with latosol soil types.
Plant materials used in the study were 12 hybrid corn genotypes consisting of 8 single cross lines ((E65 X Mr14, G688 X Mr14, E2 X Mr14, E1 X Mr14, Mr14 X N51, G682 X Mr14, G649 x Mr14, MS3 X Mr14)) and 4 comparison varieties (Bisi 2, NASA 29, Bima 3 and HJ 21). The four varieties are high yielding potential hybrid maize, specifically for Bisi-2 and Nasa-29 varieties used as a comparison of two-pronged varieties. Other materials used are Urea fertilizer, NPK Phonska fertilizer, manure, herbicide, insecticide and fungicide. The tools used include: hoes, tools, meters, calipers, scales, moisture meters, cameras and other tools that support this research.
This study used a Randomized Block Design of 12 hybrid hopes and four comparable hybrid varieties, each of which was repeated three times, so that there were 36 experimental units. Each experimental unit consists of four rows of plants (3 m wide) with a length of 5 meters.
Corn plants are planted with a spacing of 75 cm x 20 cm, with a seed system of 1 seed per planting hole. Planting holes are closed again with 250 g (1 hand) of manure per planting hole. Fertilization is carried out 2 times at the age of 10 days and 35 days, with a dose of fertilizer used as much as 300 kg of NPK Phosnka / Ha and 250 kg Urea / Ha. Fertilization is done manually, then fertilizer is closed again with soil. Weeding is carried out at the age of 21 days, using a selective herbicide. For pest control, when planting each hole Carbofuran 3G is given at a rate of 16 kg / ha, and given again at the age of plants 3-4 times if there is a pest attack. Watering is done every 2 weeks or according to the conditions of the plants and harvesting is done when physiological cooking plants.
Data collected in this study include; agronomic data (plant height, cob position height, age of male flower exit, age of female flower discharge, number of crops, number of harvesting cobs), yield and yield components (ear length, ear diameter, number of rows / ear, number of seeds / row dried seed yield). Data were analyzed using Analysis of variance (ANOVA), if there were differences between treatments, then a further test was carried out using Duncan at the 5% level.

Plant Agronomy
From the results of the analysis in Table 1, it is known that genotypes have a significant effect on plant height. Where the single cross hybrid genotype (G649 x Mr14) has the highest plant that is 251.83 cm, which is not different from the plant height in single cross genotypes E65 X Mr14 (238.83 cm) and comparable varieties Bisi-2 (243.83 cm) . While the lowest plants were the single cross hybrid genotype G 682 X Mr14 (208.00 cm) which was no different from the other 5 genotypes and from the comparable varieties of Bima-3. This shows that all single cross hybrid genotypes tested had criteria that were in accordance with the released hybrid corn varieties. The difference in plant height between hybrid genotypes is caused by genetic factors and environmental factors. Plant height shows the potential of each of the hybrid genotypes tested. According to Syukur [10], that the higher the plant, the greater the number of leaves that increase the efficiency of photosynthesis. Sari [11], also stated that high maize plants have more leaves, these plants have the potential to be used as a source of animal feed, because good animal feed sources are plants that have good vegetative growth. Crops are higher, tends to get more sunlight intensity, so the leaves will absorb more light to be used for photosynthesis. So that the resulting assimilation is high, which affects the increased dry material of plants including seeds [12].
The height of the cob from the analysis results is influenced by the genotypes of the plants tested. The single cross genotype of G 649 x Mr 14 has the highest cob position height of 132.53 cm, which is not significantly different from the location of the hybrid genotype E 65 X Mr 14 and 3 comparable varieties Bisi-2 (133.80 cm), Nasa-29 and HJ-21. While the lowest cob position is in the hybrid genotype G 682 X Mr 14 (103.73 cm) which is not different from the cob height in the other 5 hybrid genotypes of 1 comparable variety Bima-3 (109.93 cm). This means that all hybrid genotypes tested have the same criteria as the comparison varieties that have been released. According to Siswati [13], that cob height is one of the guidelines for the implementation of selection. According to Moedjiono et al. [14], that the level of corn crop breeding has a relationship with plant height and cob height, where tall plants tend to fall more easily than with short plants. According to Syukur [10] that in maize breeding, the low position of the main cob is the desired character, this is because the low cob location will be easier to accept pollen because physiologically is less blocked by leaves. The position of the cob that is balanced in the middle of the plant will be better.
Age out of male flowers is also influenced by the genotypes tested. Where the single cross hybrid genotype G682 X Mr14 has a longer male outflow age of 54 days that is not significantly different from the other 4 single cross hybrid genotypes in sequence Mr14 X N51, E65 X Mr14, G688 X Mr14, MS3 X Mr14 (52.00 -53.67 days) and 3 comparative varieties sequentially, namely Nasa-29, HJ-21, Bisi-2 (51.33-53.67 days), but significantly different from the 3 other single cross hybrid genotypes, E2 X Mr14, G649 x Mr14, E1 X Mr14 from 1 Bima-3 comparison variety (50.67 days). For single cross hybrid genotypes that are not significantly different from Bima-3, they will tend to be more early maturing or faster to harvest.
For the age of the exit the female flower is also affected by the genotype tested. Of the 8 hybrid genotypes tested, there were 5 single cross hybrid genotypes and 2 comparative varieties (Nasa-29 and HJ-21) which had a longer lifespan of female flowers of 53.67 -55.33 days, which was significantly different from 3 hybrid genotypes other single crosses namely E2 X Mr14, G649 x Mr14, E1 X Mr14 and 2 comparison varieties Bisi-2 and Bima-3 (51.67 -52.67 days). The difference occurs because it is influenced by genetic factors. Single cross hybrid genotypes that are not significantly different from Bima-3 comparable varieties have a faster harvesting age than other genotypes. This result is consistent with Singgah [15], that male flowering and female flowers have a significant effect on the tested hybrid genotype. Of the 47 lines tested, they have a shorter age compared to the comparable varieties of Pertiwi 3, so that the 47 lines will be faster than Pertiwi varieties 3. The sooner the plants begin to flower, the plants will be able to be harvested and produce results faster. The compatibility between the age of female flowering and male flowering age is very important because this is related to fertilization, Siswati [13]. This has been explained by Jones and Kiniry in Yasin [16] that synchronization of panicle formation in male and female plants guarantees the optimal fertilization process.
Anthesis Shilking Interval (ASI) is the time difference between the discharge of male and female flowers. The smaller the value of breast milk, the repair process takes place well. From the results of the analysis in table 1 on the value of ASI, it is known that 8 single cross hybrid genotypes and 4 comparable varieties tested had low ASI values. This shows that the occurrence of pollination synchronization between male and female flowers, which will have a positive effect on the filling and seed formation process. According to Nursehang [17], the smaller the Diversity Coefficient (KK), the more male and female flowering time is, the more pollination faster and faster harvesting, so the potential for better yields. The length of the ASI period is strongly influenced by environmental conditions. Short breast milk is important to ensure synchronization of flowering period in corn plants. Therefore the ability of plants to produce short milk on acidic land during the reproductive phase is one of the criteria for selecting plants that produce high on acid soils.

Yield Components
From the results of the analysis in Table 2, it is known that the length of the cob is influenced by the genotype tested. From the 8 genotypes tested there were 3 single cross hybrid genotypes which had the longest cob sequentially, namely G682 X Mr14, E2 X Mr14, E65 X Mr14 with length 18, 20 -19.43 cm which is significantly different from 5 other single cross lines and 4 comparison varieties. This indicates that the 3 single cross lines above have the potential and opportunity to be a source of new hybrid superior varieties, because they have a longer cob compared to the released varieties. Where the ear length is one component that affects the dry shell weight of corn kernels. According to Siswati [12], that the factors that influence the difference in ear length of contents on each strain can be influenced by genetic factors of each parent of the cross. The ear diameter is also significantly affected by the genotype. Of the 8 hybrid genotypes tested, there are 5 single cross hybrid genotypes that have larger cob diameters in sequence, namely MS3 X Mr 14, E65 X Mr14, E2 X Mr14, E1 X Mr14, Mr14 X N51, with diameter (4.66 -4.99 mm), which is not significantly different from the comparative varieties of Bisi-2 and Bima-3. Even though the five single cross strains mentioned above have not been able to exceed the ear diamter of the comparative varieties of HJ-21 (5.39 mm) but have been able to match 2 comparable varieties, namely Bisi-2 and Bima-3. From these results indicate that the five single cross lines mentioned above have entered the variety criteria. Explained by Mimbar [18], the relationship between the length of the cob, the diameter of the ear with the weight of the cob is increased by the length of the cob and the diameter of the cob. The character of the cob diameter can affect the weight of the cob produced.
The number of lines per ear is also influenced by the genotype. Of the 8 strains tested there were 3 single cross hybrid genotypes sequentially having more number of rows per ear, G 649 x Mr 14, E 1 X Mr 14, Mr 14 XN 51, with number of rows (14-27-15,47) , which was not significantly different from the comparison variety HJ-21 (14.67 lines), but was significantly different from the 5 other single cross hybrid genotypes from 3 comparable varieties Bisi-2, Nasa-29 and Bima-3.  For the number of seeds per row, the results of the analysis are also influenced by the genotype. There are 4 single cross lines that have more number of seeds per row in sequence, G688 X Mr14, G 688 X Mr. 14, G 682 X Mr 14, E 65 X Mr 14, with the number of seeds (35,53 -38,40 seeds ) which were not significantly different from the comparison varieties Nasa-29 and Bisi-2, but were significantly different from 4 other single cross lines and 2 comparative varieties of HJ-21 and Bima-3. According to PPPTP [19], that of the tested female parent lines has good potential to be chosen as elders for the manufacture of hybrid varieties. This is based on the characteristics of the number of lines per ear on several superior hybrid corn varieties, namely 12-16 lines such as BIMA 14-BATARA, BIMA 13-Q, PAC 759 and JK-8.
The weight of 1000 seeds is used to determine the number of seeds needed per unit of land area. If the size of the seeds is large then the number of seeds needed per unit area is more than the small seeds. In physiology of plants, seeds that have a larger size have better growing power compared to small ones. From the results of the analysis it was found that the weight of 1000 seeds in the 8 single cross hybrid genotypes tested and 4 comparison varieties were not significantly different. This shows that the 8 single cross hybrid genotypes tested have potential and opportunities to be tested further or released into new varieties, because they are in accordance with the criteria of the varieties that have been released.

Double Ear
Eight lines tested are lines that have high yield potential and have the chance to double collide. Comparative varieties that have the potential of double ear are Bisi-2 and Nasa-29. From the results of the analysis of the number of crops and the number of harvested cobs obtained lines that have the potential to produce double ear. From Figure 1 shows that the single cross hybrid genotype E2 X Mr14 has the highest percentage of double ear, which is 28%, which is significantly different from 7 other single cross hybrid genotypes and 4 comparable varieties. For single cross hybrid genotypes and varieties that have negative values in the figure show that the number of harvesting cobs is less than the number of crops. This means that there are some corn plants that do not produce cob or can produce cob but filling the seeds is not perfect. From these results indicate that the E2 X Mr14 single cross hybrid genotype has the chance to be released into new hybrid superior varieties.

Dry seed weight
From Figure 2 it is known that genotype influences the weight of dried seeds. Of the 8 hybrid genotypes tested, there were 4 single cross hybrid genotypes with the potential to weigh more than 13 tons of dry seed, sequentially ie E 2 X Mr  [20], that the higher the weight of dry seeds obtained means the higher the rate of accumulation of dry matter distributed during the process of filling seeds. Seeds are formed through the process of pollination and fertilization. Early pollination will prolong the process of filling the seeds so that it allows the seeds to accumulate more dry matter into the seeds.

Conclusion
A single cross-hybrid genotype that generates higher productivity than all the comparative varieties is (MS 3 X Mr 14) 13.87 t/ha, (E 65 X Mr 14) 13.86 t/ha, (E 1 X Mr 14) 13.64 t/ha, and (E 2 X Mr 14) 13.29 t/ha. Thus the four hybrids can be proposed to become new superior varieties. There is one single cross-hybrid genotyping that has better double-cob potency than all the comparative varieties i.e. E2 X Mr14. This hybrid has a double-copulsed potential with a percentage of 28% per ha. Thereby, this hybrid can be continued to multi-site test by exploring its excellence so that it can be removed into comparative varieties.


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Regression Analysis and Statistical Examination of Knoop Hardness on Abrasion Resistance in Lyca Beige Marbles

Marbles are secondary decomposition products formed by metamorphism of limestone. Effective classification of marble quarries in terms of quality enables the selection of a sustainable production method and safety application. This evaluation is based on physico-mechanical properties of the samples. Obtained results of physico-mechanical properties of the marbles were statistically analyzed using Stata 14 and SPSS 21 software. The marbles indicated mostly normal physical and mechanical properties. A strong inverse relationship exists between Abrasion Value and Knoop Hardness Determination that indicates a significant nonlinear relationship. Samples were distinguished into 3 groups of close similarity and related properties. The estimated value of the parameters is in the 95 % confidence interval. The equation obtained by regression analysis was used for the determination of resistance to abrasion.

Introduction
The issue of conservation of natural resources is the subject of political, social, economic and administrative systems, which directly or indirectly affect the services to be made at various level of society. With the contribution of technology and academic studies, the quality and sustainable production relationship of marbles fields has been a scientific subject. Access to natural stone with sufficient quality and required quantity has provided a working environment that can make significant contributions to science in which many disciplines can work together in watershed scale. Strengthening the sustainability of marble quarries by sustainable marble cutting potential is very important to a country and the world. The proper protection and use of a natural stone variety that serves many industrial sectors such as construction, provides benefit to a country's economy [17]. The physical and chemical properties of marbles, which are natural building blocks and its statistical data analysis, have been of high interest for researchers. Mechanical properties such as three-axis stress compressive strength, deformation, stress resistance, friction angle are examined in the determination of a marbles' quality and these data are explained by statistical operations. In the statistical analysis, the reliability of the selected experiments is investigated [4]. The physico-mechanical properties of the marble powder were determined and variance analyses (ANOVA) are made to the values found and also correlated with different samples [5] [6]. Other studies have been published in which statistical approaches were used [7][8] [9][10] [12][15] [16]. Research projects in marble area have become important for the economies of many countries [3]. Block-marble-travertine import and export data of Turkey in 2016, the country can be seen as an important place in the world market of marble [18]. There are researches in different areas related to marbles of Antalya region [11] [12]. The samples were tested for capillary mass water absorption, specific mass, total porosity, compactness, uniaxial compressive strength and mass extinction [3] [1]. However, after careful literature review, we have not encountered any study that uses regression analysis to assess Lyca beige marble data. In this study, the effect of abrasion values of Lyca beige marbles on wire cut and block, and the physico-mechanical properties were estimated using regression analysis to determine the linear and nonlinear regression model [14]. Some physico-mechanical properties and classification of the marbles in the study area were analyzed statistically unlike with other studies data.

Samples and Physico-Mechanical Properties
These 56 sample of 8 different locations taking from Elmali region for statistical analysis [3][1], were tested for their Knoop Hardness Determination (H k ), Real Density (D), Apparent Density (D a ), Abrasion Value (A) and Uniaxial Compressive Strength (UCS) using national standards at the laboratory of the Civil Engineering Department at Akdeniz University. The resulting values establish the physico-mechanical properties of the marble in the study area.

Regression and Statistical Analysis
The statistical analysis was performed on the physico-mechanical data of Lyca beige marbles using two different kind of statistical software. In this light, the accuracy of the nonlinear and linear models was examined using Stata 14. Hierarchical analysis was conducted to determine the similarities between the data of physico-mechanical properties by using SPSS 21 software [2].

Result
The description of the physico-mechanical properties of the samples is shown in Table 1 The normality test to assess the distribution of the physico-chemical properties of the samples was done in accordance with the Shapiro-Wilk test (W) indicated by [14]. The results analyzed with Stata 14 software revealed that p-values of the properties (except H k ) were greater than 0.05, as shown on Table 2, proves the physico-mechanical properties of the samples are normally distributed. The Kolmogorov-Simirnov test results were analyzed by SPSS 21 software and the results are given on Table 3. D, D a , A and UCS (except H k ) values are all higher than the lower bound value of true significance, "Asymp. Sig (2-tailed)". The also attests the null hypothesis is applicable to the distribution of these properties in the analyzed samples. The regression analysis of the data, R-Square (R 2 ), indicated the data used in the analysis were statistically sufficient (Table 4) [13]. Linear and nonlinear regression analysis was performed to measure the relationship between H k and A. In both equations, the dependent variable is taken as A and the independent variable is taken as H k . The F-statistic of both models was calculated and their p-value values were significant. The coefficients for both models were significant. All these values are given on Table 4. There was not any heteroscedastic problem for models. Errors are normally distributed and there are no specification errors in the model. Both models are valid. However, considering the RMSE (Root Mean Square Error) and R 2 (R square) values, the R 2 is a better model than the nonlinear model, which is larger and the RMSE value is smaller.
Linear Model: Nonlinear Model: The linear and nonlinear model was evaluated using the graphical illustration; this indicates a better fit (Graph 1).
The Pearson correlation coefficient is used to measure the linear relationship of two continuous variables. In other words, it is used to determine whether there is a significant relationship between the two variables. This result signifies there is a very strong inverse proportional relationship existing between the hardness and abrasion physico-mechanical properties of the samples. The p-value of the variables in Table 2 (except H k ) and Table 3 reveal a normal distribution. In this light, the Pearson correlation coefficients was carried out to test to ascertain the strength of the disparity in the variance of the physicao-mechanical properties ( Table 5). According to Pearson Correlation Coefficients, there was a very strong negative correlational relationship between the abrasive and knoop hardness properties ( Table 5).
The Hierarchical cluster technique illustrated similar characteristics do exist among some samples, such as between samples 6 and 7 (Group 1), samples 5 and 8 (Group 2), and between 1, 2, 3 and 4 (Graph 2). The samples can distinguished into two groups of high dissimilarity in the physico-mechanical properties. That is samples 2, 4, 1 and 3 show close properties similarities to each other and but highly dissimilar properties to samples 6 and 7, and samples 5 and 8 which are relatively closely similar to each other. Samples 6 and 7 are the two most closely related in terms of the physico-mechanical properties, followed by samples 5 and 4, and samples 2 and 4. The same close level of similarities demonstrated by samples 1 and 3, each of them individually demonstrates that same similarities to both samples 2 and 4 ( Table 6). From the illustration, the marbles samples from the study area can be distinguish into 3 different qualities based on their properties.

Dendogram using average linkage (Between Groups)
Rescaled distance cluster combine Graph 2. Graphical illustration of closely related samples in terms of physico-mechanical properties.

Discussion and Conclusion
On the data of the samples obtained from lyca beige marble, descriptive statistics were applied and the results were summarized and discussed above. Shapiro-Wilk and Kolmogorov-Smirnov tests were performed to determine the normal distribution of the data representing physico-mechanical properties of the marble. Linear and nonlinear regressions techniques applied to the data reveal the abrasiveness of the samples is inversely proportional to their knoop Hardness. This is confirmed by the strongly negative Pearson Correlation (r=-0.885). Besides this, every other correlational relationship exhibited between the properties is week but remains significant. D a , A and UCS all show week relationship among each other. D shows a very week relationship with both H k and A, and a week negative relationship to D a and UCS. A week negative correlational relationship also exists between D a and H k , and H k and UCS. Agglomeration schedule of Cluster Combined with Dendogram, indicates samples 6 and 7 are the two most closely related samples in terms of their properties, followed by samples 2 and 4, samples 5 and 8, and samples 3 and 1.
Group 1, samples have closely similar real density, average visible porosity and closely related abrasion value properties. Group 2 samples have average visible porosity property. Group 3 samples have average and closely related abrasive and knoop hardness properties. The most important information obtained as a result of the study is as follows: from the nonlinear regression analysis and the H k data obtained from the experiments, abrasion resistance can be easily determined by using the established equation 3. The H k can be determined easily on the field by using a hardness meter machine. By applying the proposed equation 3 with the coefficients obtained of the data from this study area, it is much faster to determine the abrasion value of marble in the area, without using the normal standard method. The estimated value of the parameters is in the 95 % confidence interval.


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A Surrogate Modeling Approach for the Development of Biorefineries

Biorefinery processes are challenged to make effective use of commercial flowsheeting software. Challenges include the lack of property data, complexity of raw materials, and emerging non-conventional processes and technologies. Surrogate models could assist by combining data-based models (by means of surrogate models) with conventional models available from flowsheeting vendors. The challenge would be to translate experimental data into property and process parameters compatible with models used by commercial software. The paper introduces an iterative approach for the systematic evaluation of such parameters. Degrees of freedom include options for pseudo-components, thermodynamic methods, and process models. The approach is applied for the modeling of a real-life biorefinery. Three case studies demonstrate the potential of the proposed scheme. The first case study has fixed options for the property and process models, while the second one has them as a degree of freedom. The third case study extrapolates the resulted metamodels to different capacities and six different feedstock types (wheat and rice straw, wood, sugarcane bagasse, banana stem, and miscanthus). Degrees of freedom expand as the cases address limited options for surrogate models, gradually incorporating additional options and, apparently, better regression results. The proposed framework could embed additional levels of sophistication using machine learning and artificial intelligence technology. The emphasis of the paper stands on the methodological framework and its demonstration with real-life examples.

INTRODUCTION
Biorefineries are promising developments for the sustainable production of food, energy, chemicals, and materials. Renewable biomass has many sources and types, including algae, agricultural, industrial, municipal, or animal waste (Vassilev et al., 2010). New technologies and new thermo-, bio-, or physico-chemical conversion paths are introduced to handle these complex and nonconventional substrates. Effectively, new products arise from these new raw materials and processing paths. Notably, a portfolio on combinations of feedstocks, technologies, and products will contain virtually an unlimited number of cases. Technological innovation, cost-efficient value chains, and sustainability are some of the challenges biorefineries have to tackle to reach industrial scale (Tursi, 2019). Process modeling integrated with optimization tools assist in the systematic analysis of supply chain, process optimization, technological alternatives, and product portfolios (RENESENG, 2018). The development of a reliable simulation model is the first step in a series of evaluation and optimization studies.
An increased number of new methods are reviewed in earlystage studies, heavily depending on flowsheeting technology (Aristizábal-Marulanda and Cardona-Alzate, 2019; De Buck et al., 2020). Moreover, there is a strong incentive to capitalize on existing technology (Ng et al., 2015). However, process modeling for biorefining processes is still facing challenges due to lack of property data, complexity of raw materials, and a constant influx of new processes and technologies (Aristizábal-Marulanda and Cardona-Alzate, 2019; De Buck et al., 2020). Data originate from a variety of sources (experimental vs. literature) and are available at different levels of confidence, especially when dealing with processes at a low technology readiness level. By a similar token, biomass is a complex complex substance of varying composition and stream properties that would need to adjust with experiments. The selection of the appropriate models still hinges on the design engineer (Upreti, 2017).
Surrogate models by means of black-box models are developed outside the remits of biorefineries. Existing methods can be generic, by fitting the fine model directly (e.g., by polynomial models), or by getting trained to fit the data (e.g., by artificial neural networks) (Li et al., 2010;Alizadeh et al., 2020). In biorefining, large-scale problems, intelligent sampling, flexibility, and uncertainty are some of the major challenges surrogate modeling has to tackle (Chen et al., 2006;Wang and Shan, 2007;Bhosekar and Ierapetritou, 2018). Caballero and Grossmann (2008) presented a methodology for the rigorous optimization of non-linear programming problems in which the objective function and/or some constraints were noisy implicit black-box functions. They substituted the blackbox modules by metamodels based on kriging interpolation and used a refining stage and successive bound contraction in the domain of independent variables until reaching the acceptable accuracy level. Boukouvala and Ierapetritou (2013) combined surrogate-based optimization tools, black-box feasibility, and noise-handling tools to optimize expensive, noisy flowsheet models. Boukouvala and Floudas (2017) presented an algorithmic optimization framework that combines variable selection, bounds tightening, and constrained sampling techniques to develop accurate surrogate representations of unknown equations. Bergamini et al. (2019) introduced a method to identify systematically process parameters that need precise measurements and reduce the data acquisition time. Hao et al. (2020) integrated simulation data and monotonicity knowledge through Bayesian optimization to reduce the required number of computer simulations for finding the optimal design solution.
Nevertheless, intelligent sampling should be adjusted to the nature of the problem so as to generate a minimum number of sampling points to reflect the real "black-box" function in areas of interest. New modeling and decomposition techniques are required to address problems with a large number of design variables. Flexible and generic metamodeling approaches are needed to deal with the variable fidelity across the design space. Hüllen et al. (2020) identified the open challenges in surrogate-based optimization as (i) sampling techniques, (ii) model development, (iii) training, (iv) validation, and (v) samplefit-validate adaptive algorithms. However, property and process parameters are ignored, even though they represent useful knowledge to exploit. The quality of the simulation model and, consequently, the quality of the metamodel depends on the successful physical and chemical representation of the real system. Existing methodologies are deprived of property-based parameters and deviate from engineering principles that are useful to retain in the model. Simulation software and libraries are not integrated into the metamodeling approach. Moreover, problems with mixed discrete and continuous variables are not sufficiently studied.
This paper introduces a generic approach to develop surrogate models for biorefining processes by integrating first-principle knowledge. The representation of the problem, the mathematical formulation, and the proposed methodology are presented in the next section. The background process and the options for the property and process models are discussed in Section Materials and Background Process. The framework is applied for the modeling of a real-life lignocellulosic organosolv technology developed by Compagnie Industrielle de la Matière Végétale (CIMV). Three case studies demonstrate the potential of the suggested procedure. The first case study has fixed options for the property and process models, while the second one has them as a degree of freedom. The third case study extrapolates the resulted metamodels to different capacities and feedstock types. The final metamodels of the CIMV process are available for the reader to use.

METHODS
This section introduces the representation of the problem, the mathematical formulation, and the proposed methodology. The systematic framework involves an iterative procedure that gradually increases the complexity of the system used for the development of the surrogate model. Initially, a simulation model is created to obtain the values of the process and property variables that were previously unknown. These variables relate to the process and property effects. Next, a surrogate model is developed to describe the simulation model. Different experiments are performed by regressing the surrogate model with the same parameters as the ones used in the simulation. If the discrepancy between the experimental and the surrogate values is not as small as desired, then additional details or subprocesses are incorporated. Finally, an optimization problem monitors the convergence of the iterative procedure and evaluates the feasible region within which the models can be used.

Problem Representation
From a systems perspective, a flowsheet is an ordered combination of process units that transforms incoming material, energy, and information streams to new output streams, which continue as feed streams to other process units. A process unit (i) stands for a piece of equipment or a process step that integrates the property and the process system. A component j stands for material (e.g., water, cellulose) that enters or exits a process unit, while m identifies which components can be present in process unit i. An ordered list L ij of components includes all the alternative representations (e.g., conventional, user-defined) and pseudo-components used to simulate complex materials present in a process unit. The property system consists of components and thermodynamic models Q j . Property model parameters include the parameters used by the thermodynamic models (e.g., critical temperature, heat of formation). The process system comprises unit operation models (Q i ) and operating parameters. Process model parameters include parameters used by the process units (e.g., operating temperature, efficiency).
Let the following sets stand for: The discrete variables of the problem are: The binary variables of the problem are: z i For the choices in Q î z j For the choices inQ j The continuous variables of the problem are: u j Property model parameters related to component j p i Process model parameters related to process unit i x ij Input stream for each process unit i y ij Output stream for each process unit i Let also the set of constraints be: h i Mass/energy balances g j Thermodynamic calculations Streams entering a process unit are transformed to exit streams by the combined effect of the property and process systems (Figure 1). Let us postulate a surrogate model for the regression of a process unit.
mass/energy balances for each jǫM g j = g j x ij , u j property models for each jǫM Given is a set of: • Processes and components M • Candidate process and property models Q i ,Q j • Experimental data to regress x ij ,ỹ ij ∈ D The objective is to compose surrogates for each process by selecting: • Models and parameters Q i p i ,Q j u j • Existing components in L m so as to minimize the error between the surrogates and the experimental data. The degrees of freedom of the model are the number of the selected components from the list L m , the selection of the thermodynamic modelQ j and its parameters u j , and the selection of the process model Q i and its parameters p i . The mathematical formulation takes the form: For a given choice of process and property models (e.g., for a known flowsheet model), ( ) reduces to ′ :

The Inner-Outer Regression Cycle
The optimization problem for the development of the surrogates can be decomposed into two subproblems. The first one considers the characteristics of the property system, and the second one considers the characteristics of the process system. The inner-outer regression cycle is the iterative procedure to solve those two subproblems. The outer cycle selects the Q i , which relates to unit operation models available for use (discrete options). Each process unit i has u i parameters to optimize. The selection of u i has a continuous nature as it relates to model parameters and flowrates that regress available experimental data. The inner cycle selects theQ j , which relates to thermodynamic models available to use (discrete options). The selection in g j relates to L m components available to use (discrete options). Given that g j are typically procedural models (e.g., external subroutines called by mass/energy balances), the degrees of freedom Q i andQ j are naturally decomposed so that ( ) and ′ can be relaxed in the form: or, in a more general form: The procedure builds surrogate models based on simulation software that follow an iterative modular sequential approach. Effectively, the procedure introduced is iterative, whereby: • binary variables z i ,ẑ j select choices in Q i ,Q j • each iteration k solves successfully the inner and outer regression problem • permutes z i andẑ j using integer cuts until convergence in the regression objective The above process can be formulated as follows: Check convergence onÊ k j and permute j using integer cuts onẑ j d) Check convergence on E k j and permute i using integer cuts on z i The convergence criteria include: where δ 1 and δ 2 are determined by the user. If the margin of error criterion is not satisfied, but the criterion for the improvement between iterations is, the number of components is increased using integer cuts (e.g., Kalvelagen, 2009). Previously selected models are excluded by integer cuts and new ones can be selected from a hierarchical list or by permutation (e.g., Thomson, 1993). The inner-and outerregression problems are actually non-linear problems (NLP) that can be addressed by a superstructure formulation or by a dedicated software (e.g., Aspen, gproms). The selection in process and property models is a mixed integer linear problem (MILP). The iterative procedure validates both, the surrogate and the fine model.

Inner Regression
For the purposes of inner regression, the set of components J is partitioned into a set of subgroups: where r accounts for different functional groups (e.g., alkanes, alcohols), each featuring different chemical candidates (e.g., methane, butanol) that could be selected or deselected. The definition of J ir is part of the problem description and could be adjusted differently for each i according to the nature of each substream involved. The inner regression solves ( ) and ′ .
With degrees of freedom that include the i. property models Q j ii. parameters of the property models (u j ) At each iteration k the property models are fixed Q k j , and the degrees of freedom only include u j .
The optimization results into an NLP, which could be solved externally, on behalf of the simulator, or internally, using internal optimization packages available.

Outer Regression
For the purposes of outer regression, the set of processes I is partitioned into a set of subgroups: where s accounts for different processing (e.g., reaction, separation), each featuring different design configuration (e.g., fermenter, double effect distillation) that could be selected or deselected. The outer regression solves ( ) and ′ .
With degrees of freedom that include the i. process models (Q i ) ii. parameters of the process models (p i ) At each iteration k, the process models are fixed Q k i , and the degrees of freedom only include p i .
• Options for p i may include pressure, temperature, number of theoretical stages, stage efficiency, etc. • Options for Q i may include reactor models (e.g., CSTR, PFR), separation columns (e.g., distillation, extraction, absorption), solid treatment (e.g., filter, crusher), etc. or combination of library models, by means of superstructures, with the degrees of freedom projected at each iteration.
The optimization results into an NLP, which could be solved externally, on behalf of the simulator, or internally, using internal optimization packages available.

Convergence and Iterations
The purpose of this stage is to i. monitor accuracy and convergence by checking and adjusting the • number of iterations • quality of the regressions • combinatorial options to consider ii. prepare new iterations considering • functional groups available in the inner regressions • process groups available in the outer regression The stage is adopted as an optimization problem: are limiting the number of process models and components subgroups: Problem 2 could be expanded with additional options that may include: • physical and chemical properties of the components involved • stoichiometric analysis of the selected chemical elements (e.g., C:H:O) • other experimental data valuable to integrate • feasibility and uncertainty analysis

MATERIALS AND BACKGROUND PROCESS
This section presents the background process and the options for the property and process models used for the case studies.

Process Description
The lignocellulosic biorefinery of CIMV organosolv offers a proven technology for the fractionation of a wide range of nonfood-related lignocellulosic resources coming from agricultural residues (e.g., straw, bagasse) (Lam et al., 2001a,b;Snelders et al., 2014), fibrous plants (e.g., miscanthus, hemp, flax, stem) (Mire, 2004;Mire et al., 2005), and forest residues (e.g., softwood, hardwood) (BIOCORE, 2014;Wild et al., 2015). The process consists of 10 process sections (Figure 2). If necessary, the cellulosic pulp and the syrup of hemicelluloses can be further hydrolyzed to glucose and xylose, respectively. Silica production is an additional option, which depends on the feedstock type and the interest in this additional investment cost.
-Handling section prepares raw materials whereby biomass may be sieved or chopped as needed. -Extraction is a patented process (Benjelloun-Mlayah et al., 2011). This first reaction stage combines biomass impregnation with an aquatic mixture of organic acids (AA/FA/H 2 O at 55/30/15 wt.%) and fractionation at 105 • C and atmospheric pressure. After extraction, the experimental value of kappa (Li and Gellerstedt, 1998), is maximum 25, which means that lignin is maximum at 4.5 wt.% dry mass.  . Peroxo-acids act as a solvent for the lignin and improve the delignification efficiency and its selectivity (Kham et al., 2005). After the delignification step, kappa is lower or equal to 12 (maximum lignin dried solid content 2.2 wt.%). -Deacidification removes the acids from the cellulosic pulp before the washing step. The operating temperature must not exceed the 105 • C to avoid degradation of C5 sugars. Total solid mass (MS) in the dried pulp does not exceed 92 ± 3 wt.%. -Pulp washing section treats deacidified pulp with an aquatic mixture of sodium hydroxide (NaOH). Bleached pulp undergoes a two-stage washing with process water, until its water content is raised at 90 wt.%. The process is patented (Delmas and Avignon, 2004). After the first washing stage, kappa is lower or equal to 7 (maximum lignin MS, 1.3 wt.%). The water content in product C6 pulp is 90 ± 5 wt.%. -Evaporation involves a number of stages that concentrate the extraction liquor from 10 to 65 wt.% dry, so that the product achieves high (the highest possible) concentration in lignin and hemicelluloses. -Lignin treatment separates lignin from the concentrated extraction liquor. Lignin treatment is a patented process (Delmas and Benjelloun-Mlayah, 2013). The total MS after the first washing stage is between 63 and 65 wt.%, after the second 55 and 57 wt.%, and after the third 51 and 53 wt.%. Lignin product comes in the form of powder. -C5 syrup concentration section separates the C5 sugars from the organic solvent after the precipitation of lignin. The maximum allowed temperature is 105 • C to avoid C5 degradation. The concentration of the C5 syrup is between 60 and 65 wt.%. -Solvent regeneration section recovers the water and the organic solvent from streams coming from evaporation, pulping, C5 sugars concentration, and deacidification sections. The desirable mass purity is 99.2 wt.% for distillate water and 92 wt.% for bottoms acids. The operating temperature should not exceed 170 • C due to corrosion issues. -Storage tanks are used for the central management of the solvent. The central tank accepts pure solvent steams (without solids), such as recycles acids from the solvent regeneration and the evaporation sections. The intermediate tank is used to shortly store streams coming from the extraction and delignification sections, which contain impurities. Both tanks accept make-up streams for the acids, so as to control the composition of the solvent (55/30/15).
It is postulated that if any handling is required, it is only mechanical; therefore, no model is developed for the handling section. Aspen Plus (RRID: SCR_017101, version 9) is selected as the flowsheeting software since is widely used in the industry and offers links (through CAPE-OPEN) with most of the commercial tools on simulation (e.g., SuperPro, gProms). Biomass components can be simulated as a mixture of conventional components available in commercial and online databanks (Wooley and Putsche, 1996;Phyllis, 2012;S2Biom, 2015). Following the findings of Rönsch and Wagner (2012), model HCOALGEN is used as the property method for the combustion correlation (option 4), and model DCOALIGT is used for the density correlation of the non-conventional components. The feasibility index (Halemane and Grossmann, 1983) is used to explore the feasible region (x L < x i < x U ) for which the surrogates can be safely extrapolated. Microsoft Excel (RRID: SCR_016137) is used for data organization and basic calculations, and GAMS (RRID: SCR_018312) is used for optimization.

Options for Property and Process Models
Following the presentation of Section Methods, CIMV has nine processes and the set I consists of: Delignification, Deacidification, PulpWashing, Evaporation, LigninTreatment, C5Concentration, Regeneration, Storage} Non-conventional materials in the CIMV process include biomass (feed stream), pulp (after extraction), glucan (after delignification), C5 syrup (product), C6 pulp (product), and biolignin (product). The complex materials can be simulated either as user-defined non-conventional components or as a mixture of pseudo-components. Data for the user-defined components are available in the supplementary material (Tables S1-S3). The set of components J is partitioned in seven subgroups: Following the presentation of Section Methods, the set of thermodynamic modelsQ j includes: Parameters of the property models (u j ) include temperature, enthalpy, fugacity, etc. Constraints for the thermodynamic calculations ensure the maximum temperature to avoid degradation of the C5 components g1 , the composition of the organic solvent g2 to g7 , the summation of compositions g8 , and the mass conservation when no reaction is taking place (g9). Non-conventional process models can be user-defined or synthesized by combining conventional models. For example, a fermenter can be introduced either as an aggregated nonconventional model or as a combination of conventional models for heating, mixing, reaction, and separation. In this work, it is a deliberate choice to leave out user-defined models and explore the performance of different configurations using conventional models. The information available limits the selection of the models. For example, rigorous reactor models cannot be used unless knowing the reaction kinetics. It is postulated that solid separation processes can be simulated based on either the split fraction, or the flow split, or the resulted purity. Reaction of solids can be a combination of stoichiometric or yield models and solid separation units. Washing of solids can be either a multiple stage model, or a combination of single stage and solid separation models. Shortcut or rigorous models can be used for columns separations. The set of processes I is partitioned in four subgroups: Parameters of the process models (p i ) include operating temperature, operating pressure, efficiency, number of theoretical stages, etc. Constraints for the mass/energy balances follow the process description h1 to h22 and the connectivity among the units h23 . The threshold for the accuracy error is set at 10% for the internal flows and at 5% for the outflows: ε ij = 0.10 i, j ∈ M i = {Extraction, Delignification, Deacidification, Evaporation, Regeneration, Storage} ε ij = 0.05 i, j ∈ M i = {PulpWashing, LigninTreatment, C5Concentration}

RESULTS AND DISCUSSION
The CIMV process treats lignocellulosic biomass and has patented products and technologies. Effectively, it is not evident which components and/or process models to choose from the ready-to-use libraries in simulation software. The quality of the simulation model affects the quality of the surrogate models. Two different case studies demonstrate the potential of the inner-outer regression procedure by solving ( ') and ( ), respectively. The initial point considers stand-alone flowsheets for each process models and biomass simulated as a mixture of pseudo-components (k = 0). A third case study extrapolates the metamodels to different capacities and feedstock types. It is possible to automate the link between the model and the simulator through CAPE-OPEN. However, in this work, the user is the link between the simulator and the model. For reasons of space economy, information about the design per process section is available in the supporting material.

Case 1: Fixed Process and Property Models ( ′ )
The type of the process and property models is fixed. Degrees of freedom are the selection of the existing components and the property models. Each iteration increases the cardinality of the subgroups. The iterations stopped because of the condition jrẑ jr ≤ N max r . The resulted models violate four process constraints (h2 = 5.64; h6 = 6.30; h9 = 4.22; h12 = 18.25). Figure 3 shows how the relative error changes when increasing the number of the components for the simulation in the feedstock stream (r = Biomass) for the extraction. The threshold for adding a new component is set at 5%. Figure 4 shows how the error in calculations is progressing during the iterations. The spike between the initial point (k = 0) and the first iteration (k = 1) is because models in the initial flowsheet are converged as stand-alone units, while k = 1 considers the models connected (constraint: h23). The increase in components cardinality has a significant impact on constraint h12 and the inflow of the evaporation unit (X_Evap). This is due to the fact that those two factors indicate the solid treatment in the extraction stage. The total flow of the lignin product (Y_LigT) mainly includes the components from the lignin subgroup, which is not significantly affected. The outflow of the acetic acid after evaporation (Y_AA_Evap) improves slightly when the property model changes from NRTL to NRTL-HOC, but it is not significantly improved farther after that. Table 1 compares the composition of the product streams as calculated by the experimental groups to those calculated by the model. The error in the composition per component is <5%, which is within the acceptable margin of error.
Case 2: Inner-Outer Regression Cycle ( ) Degrees of freedom are the selection of the existing components, the configuration of the process models, and the type of the process and property models. Figure 5 shows how the error in calculations is progressing during the iterations. The spike   between the initial point (k = 0) and the first iteration (k = 1) is for the same reason as in case 1. For k = 4, the model NRTL-HOC is regressed with experimental data. This regression of the model parameters results in a drastic improvement on the evaporation outflow (Y_AA_Evap). Iteration k = 7 chooses the user-defined components (Biomass, Pulp, Glucan_d) and improves significantly the error in the inflow of the evaporation unit (X_Evap). Iteration k = 9 selects solid_separation units for the simulation of solids washing in the lignin treatment section and the error in the lignin product (Y_LigT) drops below 5%. The resulted models satisfy the convergence criteria and all the constraints, so the iterations stop.
FIGURE 5 | Error vs. iteration-case 2. Figure 6 compares the regressed model with some of the existing thermodynamic models for P = 1 atm. It seems that the available thermodynamic models cannot predict correctly the azeotrope between FA-H 2 O (107.2 • C and 77 wt.%). Figure 6A shows that after 30 wt.% in water, NRTL-HOC overestimates the vapor composition in the solvent mixture, while the other models underestimate it. Figure 6B shows that only model NRTL predicts well the vapor phase of AF, but when it comes to the AA vapor composition (Figure 6C), it underestimates it until around 30 wt.% H 2 O. Figure 6D shows that model NRTL-HOC overestimates the temperature of the AA vapor phase, model UNIF-HOC underestimates it, while NRTL underestimates it until the region of 0.75-0.82, after which it overestimates the temperature of vapor AA-H 2 O mixture.
The question is what the impact of the optimization of the property parameters is. Table 2 shows how the mass balances of  the distillation column change with the different thermodynamic models. For all cases, the distillation feed and the design specifications are the same. Even though the error of the NRTL and NRTL-HOC model is within the 5% limit, this deviation provokes significant errors in the mass balances of the organic solvent (AA and FA). The most notable impact of the improved thermodynamic models is on the energy balances (Figure 7). The mass balance estimations of the NRTL-HOC model, for example, are the closest to those of the regressed model, but the reboiler duty is overestimated by 43%. Model UNIF-HOC, on the other hand, overestimates the reboiler duty only by 7% but has large deviations in the mass balance calculations. Table 3 compares the error per component composition in the product streams. The accuracy error is <0.5%.

Case 3: Model Extrapolation
Metamodels from case 2 are extrapolated to different treatment capacities and feedstocks. The inflow is ranged from 8 to 1,000 ktn/year (dry biomass). Figure 8 shows how the accuracy error is affected as the capacity of the model changes. Results show that the model is resilient to capacity fluctuations. The accuracy error in the composition of the products remains below ±3%.
The highest deviation appears in the total flows, where the model underestimates the total flow by −2.5%. Figure 9 shows how the accuracy error of the C6 pulp flow changes with different feedstock types. In all cases, the models underestimate the total flow. The error is below 3%, which is within the acceptable margin of error. The metamodels produced for the mass flows are linear per biomass type and per component: rj · X j where n is the feedstock, X the mass inflow on dry basis, Y the mass outflow on total basis, r the component subgroups (product streams), j the components, α the parameter for the total mass flows, and β the parameter for the component flows. The parameters α and β are available in the supplementary material (Tables S23-S34). The accuracy is within 5% for 8 ≤ X ≤ 1,000 ktn/year (dry biomass, annual operation, 8,000 h/year). Surrogates about the consumption of utilities (mass and energy), the production of effluent streams, and the cost estimation depend on the integration strategy. For example, for a capacity of 170 kt/years dry biomass, the equipment cost is estimated at $157.32 MM ($2019) (±20%) when choosing a double effect distillation column in the regeneration section and at $153.93 MM (2019) (±20%) when choosing a single effect.

CONCLUSION
Existing techniques develop surrogates by fitting the model to the available data and then evaluating its safe use within known boundaries. No method accounts heretofore the engineering principles. The quality of the simulation model and, consequently, the quality of the metamodels depend on the successful physical and chemical representations of the real system. This paper introduces a new iterative method to assist in the development of low granularity models and the decision-making of biorefinery flowsheeting. Two different case studies demonstrate the potential of the inner-outer procedure. The first case study solves the regression problem with fixed options for the process and property models. Degrees of freedom include the selection of the existing components and property models. The second case study has degrees of freedom the selection of the existing components, the configuration of the process models, and the type of the process and property models. A third study extrapolates the resulted metamodels to different capacities and feedstocks.
The first case results in metamodels in relatively good agreement with the experimental data. However, process units with a high dependency on the property models (evaporation and distillation) do not meet all the process constraints. This outcome   indicates a shortcoming of the initially given thermodynamic models. The second case study results in metamodels that meet all the process and property constraints. The parameters of the initial thermodynamic model are regressed with the experimental data. The improvement of the thermodynamic calculations in the simulation model allows the selection of more rigorous process units for evaporation and distillation, further improving the estimation of the mass and energy balances. Case 3 extrapolates the metamodels to different capacities and feedstock types (wheat and rice straw, wood, sugarcane bagasse, banana stem, and miscanthus), without losing in liability. The inner-outer regression cycle produces metamodels that gain in accuracy, flexibility, and simplicity and gives valuable insight into the shortcomings of the available simulation models by filling the gap through regression with experimental data. However, the introduced method is limited by the availability in libraries of process and property models of the selected simulation software. Each cycle is tailored to the process under study and the simulation software.