optimization

 Mayonnaise is one of the world’s most popular sauces. It is a semi-solid oil-in-water emulsion made by mixing vegetable oil, egg yolk, water, vinegar, and other ingredients. Due to the high calorie and cholesterol content of egg yolks, excessive consumption of mayonnaise can lead to health-related problems. Major issues faced by mayonnaise producers include fat oxidation during storage, which leads to off-flavor and taste, as well as reduction in nutritional value and food safety. These factors may negatively affect on the consumer acceptance of mayonnaise. With increasing consumer demand for low-fat and low-cholesterol foods, low-fat mayonnaise has significant consumption potential. One of the consumer demands is to reduce the fat content in mayonnaise and salad dressing. Fats play many functional roles in food emulsions, contributing to the taste, appearance, texture, and shelf life of the product in specific ways. Therefore, it is challenging to maintain the quality of traditional products when preparing low-fat foods. It is possible to select specific fat substitutes in particular amounts to create a product with a texture similar to traditional mayonnaise. Light mayonnaise often contains certain fats added to help stabilize the emulsion and thicken its consistency. This research studied the possibility of using aloe vera gel as partial fat substitute in mayonnaise.

All the powdered ingredients were mixed together with eggs, water, and half of the required amount of vinegar in a blender for 3 minutes. To form an emulsion, oil was added in two stages while stirring together with starch and gums for 5 minutes. Mixing oil and the aqueous phase simultaneously leads to the formation of a water-in-oil emulsion. Finally, the remaining vinegar was added and mixed for 3 minutes until the fat particles were evenly dispersed in the sauce. Aloe Vera gel was also added in proportion to the reduction of oil according to the formulation of the samples. The optimal mixture design was used for three independent variables: oil A 15-60%, gel B 0-55%, and water C 0-10%. The multiple combinations of these variables led to an experimental design with 16 samples that were determined using Design Expert software, and the physicochemical, rheological, and textural characteristics of the samples were investigated. Additionally, suitable regression equations and mixed contour diagrams were obtained by this software for each response.

The results showed that increasing the replacement percentage of aloe vera gel and decreasing the percentage of oil led to an increase in pH and a decrease in acidity, stability, textural properties, and transparency of the samples. To describe the rheological properties, Bingham's model was used, and the viscosity parameters and Bingham's yield stress were investigated with an increasing the percentage of aloe vera gel replacement. Additionally, the apparent viscosity of the samples exhibited a decreasing trend and thinning behavior with shear. These changes in characteristics are attributed to the effect of adding fat to the mayonnaise formulation. The optimal values of independent variables in the production of low-fat mayonnaise were oil (40%), aloe vera gel (30%) and water (0%), respectively.

This research showed that aloe vera gel can be used in the formulation of mayonnaise as apartial substitute to oil. Due to the strong demand from consumers for this product, reformulating this high-fat emulsion is a fundamental need in the food industry. The use of aloe vera gel offers many benefits, including reducing cholesterol and fat levels overall, increasing microbiological stability, and, in some cases, lowering manufacturing costs.

Chickpea protein is considered a high-quality natural protein and can be used as a nutrient or the main ingredient of foods, useful for human health.  In this study, the effect of four independent variables including time (20-60) minutes , temperature (4-35) degrees of Celsius, pH (8.50-10) and solid to solvent ratio of (1:10 – 1:15) was investigated on the optimization of the physicochemical properties of Anna variety chickpea protein, and its functional properties, including foam formation capacity and stability (in 30 and 180 minutes). For this purpose, 30 standard runs were performed using the response surface method, central composite design including 6 repetitions at the central points. The maximum foam formation capacity and stability was obtained with optimal conditions of temperature of 4.055 °C, time of 54.27 min, pH value of 8.517 and solvent to solid ratio of 1:10.220. The highest foam stability was observed after 40 minutes, at pH equal to 8.5. The results of this research showed that the chickpea protein of Ana variety could be used as part of food formulation, which increases the nutritional value and functional characteristics of the product.

The growing population and increasing demand for natural resources present significant challenges. The nexus approach, which involves modeling and optimizing the consumption of water, energy, and food, enhances resource efficiency and ensures the sustainability of interconnected systems. This study focuses on investigating, modeling, and optimizing the water-energy-food nexus for paddy production in Rasht County. The objectives were pursued through two scenarios: high-yield paddy production and agricultural consolidation, utilizing the LEAP, WEAP, and MGP models. Data were collected from farmers, organizations, and library sources. Optimization was conducted using the NSGA-II algorithm. Inputs such as fuel, electricity, agricultural machinery, chemicals, organic materials, labor, and water were analyzed. The water resources considered included those for drinking, industrial, and agricultural purposes, as well as rivers, channels, ponds, and groundwater. The results revealed that the average energy consumption and production in the high-yield paddy variety scenario were higher compared to the agricultural consolidation scenario. The average water consumption in both scenarios showed no significant difference until 2040. Modeling results indicated that under ideal conditions, the RMSE, R², MAPE, and EF values were 353.110, 0.9032, 10.831, and 0.891, respectively. The optimization findings demonstrated that under ideal conditions, water and energy consumption, and paddy production were reduced by 0.94%, 0.53%, and 3.26%, respectively.

This study investigates the optimal management of water and energy resources in the Sefidroud irrigation and drainage network of Guilan province. Given the critical role of these resources in agriculture, along with water scarcity and increasing food demands, the need for an integrated approach is becoming more evident. A multi-objective optimization model under uncertainty was developed for this study, aiming to minimize agricultural water shortages and maximize hydropower generation from the Sefidroud reservoir dam. The developed model was solved using the NSGA-II algorithm, and the irrigation requirements for rice and tea were calculated based on the soil-water balance method. The results indicate that water shortages vary across different confidence levels, with the central irrigation zone experiencing the highest deficit. Additionally, rice cultivation, especially in June and July, faces more significant water shortages, whereas tea cultivation does not encounter major water scarcity issues. This research highlights the necessity of optimal resource management and precise planning to prevent water shortages during critical months. The findings have the potential to inform effective decision-making aimed at sustainable agricultural development.

Althaea officinalis is one of the plants containing mucilage, the therapeutic effect of its mucilage on the mucous membrane of the tissue and the healing of stomach ulcers has been proven. The mucilage of this plant consists of L-rhamnose, D-galactose, galacononic acid and D-glucuronic acid. The polysaccharides found in this plant have antioxidant properties. The main antioxidant activity of this plant is related to its α-tocopherol composition. Gums extracted from different sources have different functional and rheological properties than each other, and the conditions of extraction have a significant effect on these properties. In this research, the extraction efficiency of Althaea officinalis mucilage as a new hydrocolloid source was studied. For this purpose, the effect of microwave power (250-850W), water-grain ratio (50-90) and time (2-10 minutes) on the extraction efficiency were optimized. The gum extraction efficiency ranged from 4.4% to 11.6%. It can be seen that the lowest amount of extraction was obtained at a ratio of water to grain of 70, time of 2 minutes and power of 550 watts, and the maximum amount of extraction was obtained at a ratio of water to grain of 80, time of 4 minutes and microwave power of 700 watts. According to the results obtained from analysis of variance Data, quadratic model is the best model to describe data. In low powers, increasing the ratio of water to seeds led to an increase in extraction efficiency. The increase in extraction efficiency by increasing the ratio of water to seeds can be attributed to the presence of more solvent, which intensifies the driving force for the transfer of gum mass from the seeds.

Accurate estimation of saffron water demand is essential for sustainable water resource management in saffron-growing regions. This study examines the optimization of the Artificial Neural Network (ANN) model for predicting saffron water demand using the hybrid Coati Optimization Algorithm (COA). The performance of the ANN-COA model was compared with ANN, ANN-GA, ANN-PSO, ANN-MFO, Quadratic Regression (QR), Tree Regression (TR), and Pattern Regression models. Input data included temperature (minimum, maximum, average), wind speed, relative humidity, net radiation, and day of the year. The results showed that under conditions using all climatic parameters, the ANN-COA model achieved an R² of 0.995 and a Mean Squared Error (MSE) of 0.0001 for the Mashhad station, and an R² of 0.973 and MSE of 0.0005 for the Birjand station, indicating acceptable accuracy in predicting saffron water demand. Additionally, under conditions with limited climatic parameters, the ANN-COA model, using maximum temperature, wind speed, and day of the year, exhibited the best performance in predicting saffron water demand. Based on the findings of this research, hybrid neural network models show superior accuracy in estimating saffron water demand with minimal climatic parameters compared to other data mining models.

The Keratinase enzyme produced by Bacillus is used to break down raw keratin and produce animal and poultry feed. The purpose of this study is to investigate the optimization of keratinase-producing bacillus to decompose and provide a protein source for animal feed.

This experimental-laboratory study was conducted in 1401 on chicken slaughterhouse samples. Heat treatment, alcohol treatment, and polymerase chain reaction were used to isolate Bacillus licheniformis (B. licheniformis) strains. To optimally check keratinase activity, keratinase was precipitated by ammonium sulfate method. Comparison of the mean changes and standard deviation of keratinase before and after optimization was done with Tukey's one-way analysis of variance and post hoc test and data analysis was done with SPSS-26 (p<0.05).

(85.57 ± 0.64 units/ml) keratinase activity of B. licheniformis strain PVKR15 was higher than other strains. The total keratinase activity after optimization was 800 units/ml and before optimization was 500 units/ml. pH, temperature, heating time, creatine source, and creatine concentration to optimize conditions respectively; 11, 37 degrees Celsius, 72 hours, and azocreatine at a concentration of 10 g/l. There was a significant difference between the keratinase activity of B. licheniformis PVKR15 with pH, temperature, creatine source, creatine concentration, and incubation time (p<0.05).

B. licheniformis PVKR15 has a useful and effective role in the growth of animals to break down keratin as a probiotic supplement for livestock and poultry feed

Curcumin is a bioactive, lipholitic natural compound with anti-Oxidant, anti-inflammatory, anti-microbial and anti-cancer activities. Bioavailability and beneficial effects of Curcumin are restricted by its low solubility in water. Gum Mastic is a natural resin obtained from broad-leaved variety of Pistacia lentiscus. In this study Gum Mastic was modified by solvent treatment at the first. Then microparticles containing Curcumin were prepared by emulsion solvent evaporation method. Effects of 4 factors, including the type of Gum Mastic (natural/ modified), the ratio of natural and modified Gum Mastic, the Gum Mastic -Curcumin ratio (1-3) and the amount of Magnesium Stearate (10, 15 and 20% basis Gum Mastic weight) were investigated by combined statistical design in Design-Expert software. The stability, biological protection, encapsulation efficiency and loading capacity and release rate were investigated in order to determine the optimal microcapsule. The results showed that by increasing the ratio of Gum Mastic to Curcumin, the of modified Gum Mastic ratio and decreasing the amount of Magnesium Stearate, the encapsulation efficiency and loading capacity were increased and encapsulated Curcumin loss percent was decreased after 14 and 30 days. Optimal microcapsule was obtained with the ratio of gum mastic to curcumin 3:1, the proportion of modified mastic gum 100% and magnesium stearate 10%.

Due to the severe water crisis in the Mahabad Dam, it is felt necessary for the continuous management of water resources, the necessity of forecasting and optimizing this dam. Planning to optimize the use of water resources is an optimization problem, which requires the use of complex, multi-variable and multi-constrained optimization methods. In this paper, a new method is proposed to predict reservoir dam storage. Continuous Genetic Algorithm (CGA) is a new method in the field of evolutionary computing that optimizes the optimal response of various problems and is applied in the Mahabad dam. This algorithm, in comparison with other algorithms, optimizes the speed of optimal response. In this research, annual optimization for Mahabad reservoir for the whole year was conducted to achieve the optimal policy using continuous genetic algorithm. The results of this research showed a minor error (2.4%) in the implementation of the algorithm. In addition, the application of continuous genetic algorithm to the annual optimization problem and its comparison with observational data suggests the very high success rate of the proposed method.

Saffron, a member of the lily family, is highly valued for its low water requirements, potential for job creation, and medicinal properties. Despite the high costs associated with traditional saffron cultivation, the use of tissue culture is recommended to enhance cultivation practices and produce disease-free seedlings. In vitro cultivation can significantly boost the production rate of new seedlings, with success largely dependent on the hormonal composition used. Additionally, carbon nanotubes, which have various applications in biology, have demonstrated a positive impact on plant growth. This study employs the response surface methodology (RSM) to optimize the saffron culture medium with carbon nanotubes and to identify the appropriate hormonal composition.
 
This research was conducted using in vitro culture techniques and a RSM design, comprising four replications. The factors investigated included concentrations of 2,4-D (0.2-0.5 mg/L), BAP (1.5-6 mg/L), and multi-walled carbon nanotubes (30-70 μg/mL). The culture medium and equipment were sterilized in an autoclave at 120°C and 1.5 atmospheres. Pedicel shells were removed, washed, and then immersed in Vitex solution for 30 minutes. The explants were placed in culture dishes and maintained under controlled light and temperature conditions. After five weeks, callus induction characteristics, such as callus induction percentage, diameter, and fresh weight, were measured. The Box-Benken statistical design was utilized for data analysis, and necessary transformations were applied based on software recommendations. A quadratic function was generally selected for the fitted model, even in cases of non-significance, and response surface curves along with contour diagrams were used to interpret the interaction effects of the traits. Tissue culture optimization aimed at maximizing callus formation, fresh weight, and diameter, with the highest model fitness identified as the optimal environment
The study's findings indicated that the concentrations of nanocarbon and 2,4-D significantly affected the number of calluses produced. At lower concentrations of 2,4-D, increasing BAP resulted in a higher number of calluses, while at higher concentrations, this effect remained positive. The maximum number of calluses was achieved with specific combinations of 2,4-D and BAP concentrations. High levels of BAP and carbon nanotubes increased the callus production percentage to 174%, whereas at lower concentrations, it was below 85%. The ratios exhibited a saddle function, with the highest number of calluses observed at low concentrations of 2,4-D and nanocarbon. Additionally, the linear effects of 2,4-D and carbon nanotube concentrations were significant at the 1% level, while the effect of BAP was not significant. Elevated concentrations of 2,4-D negatively impacted callus diameter, whereas increasing nanocarbon at high concentrations contributed to an increase in diameter. Ultimately, raising the concentration of 2,4-D led to an increase in callus induction percentage from 79.68% to 92.185%, although high concentrations had a detrimental effect. The highest callus induction percentage was noted with a specific combination of 2,4-D and carbon nanotubes.
 
The experimental results demonstrated that carbon nanotubes and the hormones BAP and 2,4-D significantly influenced saffron callus induction. High concentrations of 2,4-D exhibited the most substantial effect on the number of calluses produced. Furthermore, the application of BAP hormone up to a certain concentration increased callus number, but higher concentrations resulted in a decrease. Finally, the percentage of callus induction was positively affected by carbon nanotubes, while other characteristics did not show significant effects

In recent years, increasing attention to the health and quality of food products has led to the importance of food processing using new methods. One of the significant challenges in this field is the turbidity of fruit juices, particularly red berry juice. An effective way to reduce turbidity is by using natural absorbents. In this regard, banana peel is introduced as a natural absorbent that can significantly contribute to the reduction of turbidity in red mulberry juice. Known for its rich compounds, including pectin and plant fibers, banana peel is recognized as a potential source for absorbing suspended particles and reducing turbidity. The primary materials studied in this research are red berry juice and banana peel. The berry juice prepared for this research had a Brix value of approximately 78, which was reduced using distilled water through a series of 15 dilutions. The banana peel was dried at a mild temperature of 45 oC in a laboratory oven and then ground to a uniform size of 1 mm using industrial sieves. The study compared neural network and factorial methods for reducing the turbidity of red mulberry juice with the natural absorbent of banana peel. The independent variables considered included temperature, time, and stirrer speed, each investigated at four different levels, with the experimental design employing a factorial approach. The responses measured were the percentage reduction in turbidity and the percentage reduction in absorbent efficiency. The results showed that the maximum reduction in turbidity (47.22%) with the lowest reduction in the efficiency of the natural absorbent (57.65%) was achieved at a temperature of 30 oC, with surface absorption duration of 3 hours and a stirring speed of 300 rpm. Additionally, a neural network was utilized to predict the two dependent variables as functions of the independent variables. The neural network modeling demonstrated high accuracy in predicting the target variables, with mean relative error (MRE) values of 2.06% and 0.90% for the turbidity reduction percentage and the absorbent efficiency reduction percentage, respectively. In contrast, the factorial method yielded MRE values of 4.58% and 6.04%, thus significantly enhancing the prediction accuracy for the two dependent variables. Banana peel, as a natural absorbent, was effective in reducing the turbidity of red mulberry juice. Furthermore, the factorial method proved to be effective as one of the experimental design approaches for identifying optimal operating conditions in the turbidity reduction process. The use of a neural network for predicting the results of laboratory research demonstrated a high degree of confidence in modeling outputs. In this study, the neural network provided improved predictions for the two dependent responses, leading to enhanced outcomes in both turbidity reduction and absorbent efficiency reduction.

One of the main causes of widespread mortality in farmed shrimp is the white spot syndrome virus (WSSV), which can lead to rapid and significant losses within just a few days. Timely diagnosis can prevent extensive financial losses. The only reliable early detection method for this virus is molecular. Primers compatible with each other were designed from the viral genome for the VP28 region and the protein kinase-related region, and from the shrimp genome for the S18 ribosomal region as an internal control. After optimizing the primers for multiplex detection of WSSV, DNA extracted from 200 shrimp samples suspected of being infected with the virus was compared using two optimized kits and the IQ2000 reference kit (from Taiwan). Of these, 94 samples tested positive (infected with the virus) and 106 samples tested negative, with results matching the reference kit except for two samples. The sensitivity and specificity of the designed kit, compared to the reference kit, were 98% and 100%, respectively. Based on these results, the optimized one-step method from this study can be used as a replacement for or in conjunction with the reference kit.

Moghaddasi et al. (2010a,b) did a research on optimization of water allocation and distribution in irrigation network of Zayandeh Rud catchment by the use of non-linear methods of collective intelligence and genetic algorithm. In the above mentioned study, optimization has done by 4 sub-models of water distribution optimization during crop growth periods, water allocation optimization among network crops, water distribution optimization among different networks, and annual optimization of agricultural water proportion of Zayandeh Rud. Comparing the results, it can be assumed that nonlinear programming )NLP) and consequently particle swarm optimization )PSO) methods have priority to the others.in addition the study confirmed that the optimization method associates 36% more income. Management of irrigation water resources in the agricultural sector, as the most important renewable but limited resource, has been the most important challenge of the current century and has resulted in major concerns around the world. The aim of this study was to allocation irrigation water and cropping area under uncertainty with emphasis on water use efficiency (WUE) and relative irrigation supply (RIS) indices and the results were compared to the actual management in  Marun Irrigation Network.

This research proposes an optimization model for investigating foodsheds in Iran. This model was used in Tehran province. In our country, no research has been done on the optimization of foodsheds. The purpose of the optimization process in this research is to allocate the optimal food basket produced from the origin (four production areas of Tehran province) to the destination (16 consuming cities of Tehran province) by applying the minimum distance. optimization was done with the help of Lingo software to allocate the food produced in the studied area in order to meet food needs of the relevant population in the shortest possible distance. Also, the average distance between food producer and consumer was estimated according to the amount of allocation. According to the objective function (minimization of the distance) and the input data (supply and demand), the Lingo software has allocated the entire production of the province, which is supplied from four regions, to the demand regions. The act of allocation has been done according to the smaller distance and more demand, that is, the allocation has been made to the points that have the least distance to the supply points (they are closer and as a result the cost is reduced) and also their needs are more. Therefore, by examining the transportation routes from the producer to the consumer, the amount of food allocation (with the minimum distance) and the average distance between the producer and the consumer, the policies of the Food, agriculture and also the environment will be done with a broader view.

Due to the location of Iran in an arid and semi-arid climate, the optimal use of water resources systems and better management in water shortage conditions are necessary. One of the appropriate tools in the field of water resources management is the use of simulation, optimization techniques and the combination of simulation and optimization methods. The main goal of this research is to provide a solution in which, according to the capacity of the reservoir, in addition to achieving the acceptable reliability of supplying demands in the whole period, the percentage of meeting the needs in dry months also increases.

To achieve the optimal operation of the water resources in the Marun and Jareh dams located in the south of Iran, the multi-objective particle swarm optimization (MOPSO) algorithm is linked to the WEAP model to provide a new structure for the water resources management, especially in periods of low water by obtaining the optimal values of water release from the reservoir.In this structure, the WEAP simulator is called directly in the MATLAB environment and executed by the optimization algorithm. In this research, for optimal operation of the system, the amount of water released from the reservoir every month is considered as a decision variable. However, due to the large number of available variables and the high volume of calculations, in the body of the MOPSO optimization model, the flow release coefficients that are considered on a monthly basis are used. Based on these coefficients, every month, a percentage of the water volume in the Marun and Jareeh reservoirs is released to supply the needs of the downstream uses, and the rest is stored for the better management of the reservoir, especially in water shortage conditions. Therefore, according to the application of these coefficients on a monthly basis, 12 variables are considered for each dam and 24 decision variables in the whole multi-dam system in the entire next 30-year operation period.

The optimization process is carried out using the MOPSO multi-objective algorithm. The number of iterations of the algorithm to reach convergence is considered to be around 1000. Finally, after the optimization, according to the population size of 48 and the implementation of the MOPSO model for 1000 iterations, the solutions close to to optimal are obtained, and the optimal exchange curve (Pareto-optimal front) between the optimization objectives (the function of maximizing the supplying percentage and the function of minimizing the violation of the minimum operation level) is achieved (Figure (1)). In Pareto curve the solution with the least amount of penalty due to the violation of the reservoir operation capacity and the highest supplying percentage is chosen as the best answer. Then, these optimal variables are entered in the WEAP surface water model.The average percentage of supplying the needs and the level of reliability of meeting the needs according to Table (1) for different uses in optimization scenario. According to this table, in this scenario, the drinking and industrial demands are fully provided in all months. Also, the average percentage of meeting the agricultural demands of the Marun and Jarrahi basins for August, September, October and November was improved by about 15 to 16%, which is significant and shows a decrease in the severity of failure in these low water months. The supplying percentage in July also increases by about 7%.

The results obtained from the implementation of the optimizer model showed that the percentage of demand supply in the months that was 0% in the reference scenario reached 30-60% and in most of the dry months, it was calculated around 45%. This showed that the optimizer model was able to reduce the failure severity in the worst case and in years with three to eight consecutive dry months. The results showed that according to the application of hedging in the model, some of the need is stored in the high water months to be consumed in the low water months. This research showed that planning water resources and allocating them to existing uses only by relying on maximizing the reliability of supplying needs in the entire period, especially in areas with dry climates where we inevitably face severe water shortages in several months of the year, is not a suitable solution and leads to irreparable financial losses and social consequences. Instead, using the solution of this research will lead to better management of the reservoir and reduce the severity of the failure to supply the needs in the dry months of low water.

In recent decades, the issue of climate change has become one of the global issues and has affected the agricultural sector. The continuation of agriculture regardless of the water shortage crisis has had an inappropriate effect on the sustainability and growth of this sector. On the other hand, the destructive effect of excessive use of chemical fertilizers and pesticides on water, soil, health of ecosystems, humans and other living beings is undeniable. For this reason, the void of using an efficient model that can provide all economic and environmental aspects at the same time was completely felt. The aim of this study was to provide an optimal cropping pattern using the integrated method of Goal and Grey planning. For this purpose, the farmers of the agronomy sub-sector of Tajan Basin were selected as the statistical population. In this regard, time series information was collected from the aggregation of the average data of 401 settlements located in this area during the years 2017-2021 from the annual reports of experts.

The Linear Programming (LP) Model quantifies an optimal way of integrating constraints to satisfy the objective function to optimize crop production and profits for irrigation farmers. To use LP, one must convert the problem into a mathematical model. To do this, an objective such as maximizing profit or minimizing losses is required. The model must also include decision variables that affect those objectives, and constraints that limit what user can do. Therefore, the LP Model is a single-objective method. Goal Programming (GP) is an extension of LP in which targets are specified for a set of constraints. GP is used to perform three types of analysis: Determining the required resources to achieve a desired set of objectives. Determining the degree of attainment of the goals with the available resources. Providing the best satisfying solution under a varying amount of resources and priorities of the goals. Thus, the GP model is a multi-objective method. The Grey system theory is identified as an effective methodology that can be used to solve uncertain problems with partially known information. Grey modelling approach uses accident data for estimating the model parameters. The model can reflect the dynamics, balance the conflicting the multidimensional targets of cropping patterns, and promoting the sustainable use of cultivated land. For achieving different goals in unstable economic and environmental conditions, we used a Goal-Grey model that was obtained from the integration of Goal programing and Grey Programing. The Goal-Grey model, by considering the uncertainty in the data, leads to overlap between the economic and environmental goals and provides the scope of cultivation for the selected products.

By estimating the Linear Programming (LP) Model, crops like wheat and canola are removed from the cropping pattern, while the cultivation areas for barley and high-yielding long-grain rice increase by 644% and 31%, respectively. In contrast, the cultivation areas for high-quality long-grain rice and maize decrease by 89% and 10%, respectively. Implementing this model boosts the gross profit of farmers in the Tajan region by 14% solely through adjusting the crop composition, without altering the current input levels. Additionally, the findings show that applying the LP Model results in fertilizer savings of 5%, 13%, and 10% for phosphate, nitrogen, and potash, respectively. The amount of herbicide and fungicide consumption in the LP Model is exactly equal to the current model of the region. However, the implementation of this model will lead to a 5% increase in the consumption of insecticides poison. The amount of irrigation water consumption in the LP Model was calculated to be 2% less than the current model of the region. In addition, the results indicate that by estimating the Goal-Grey Model, only canola is removed from the cropping pattern. Also, in order to achieve the defined goals in this study, the cultivation area of wheat and maize should be equal to 208 and 7356 hectares respectively. However, the flexibility of input usage enables adjustments to other crop cultivation areas, facilitating high-quality long-grain rice production on 970 to 18,157 hectares. Plus, the cultivation area of long-grain rice can vary from 7654 to 9995 hectares. In this model, barley can be removed from the crop composition like the linear pattern or cultivated on a maximum of 2553 hectares. The implementation of the Goal-Grey model will lead to a maximum 2% increase in the gross profit of the farmers of Tajan region compared to the current model of this region. Also, by implementing the Goal-Grey Model, on average, phosphate, nitrogen, and potash fertilizer consumption is saved by 16, 27, and 20 percent, respectively. In addition, with the implementation of the Goal-Grey Model, the consumption of agricultural pesticides will decrease from 733 to 355 thousand liters on average.

The LP Model is designed based on current regional conditions; however, as a single-objective model with fixed parameters, it lacks the flexibility to offer an adaptable program for farmers during drought or wet periods or when inputs are limited. Findings indicate that under current conditions, there is excessive use of chemical inputs and irrigation water. By accounting for data uncertainty, the Goal-Gray model addresses these limitations, balancing economic and environmental objectives and defining a cultivation range for selected crops.

Lignocellulosic biomass, a promising renewable resource, has gained attraction as a potential alternative for biofuel production and climate change mitigation. Agricultural practices often generate significant quantities of lignocellulosic residues (e.g., crop stalks), which are frequently abandoned or burned, leading to adverse environmental impacts. Wisely collection and conversion of these residues into bioenergy could offer a twofold benefit: reducing environmental harm and partially displacing fossil fuels. The study aimed at evaluating the potential of lignocellulosic biomass from agricultural activities in Golestān province as a sustainable source for renewable energy production; and estimating the transportation costs associated with the biomass feedstock within the context of a pilot project.

To do so, we employed satellite image processing (Sentinel-2 and Landsat 8) to generate land-use maps and identify the spatial distribution of biomass supply sources from four major crops (wheat, soybean, rice, and rapeseed) and estimate the available biomass volume from each crop in energy units (kWh). Subsequently, an optimization model was developed to design a biomass-to-energy supply chain network for the study area.

The overall classification accuracy and kappa coefficient for wheat and rapeseed using Sentinel images were 82% and 0.74, respectively. Soybean and rice classifications using Landsat images achieved 76% and 0.63 accuracy, respectively. Area estimation identified 84,104 farms exceeding 2 ha, encompassing a total area of 468,000 ha. This represents an 11% bias compared to statistics provided by the Iran's Ministry of Agriculture organization for the same period. The optimistic scenario suggests a potential harvest of 3.8 million kWh of energy from the identified farms. The optimization model, considering both fixed and variable transportation costs, determined that locating three biorefineries would be sufficient to process the biomass and generate electricity. Transportation costs for this scenario were: US$222 million fixed cost, US$1.599 billion variable cost, and a total cost of US$1.821 billion. The optimal scenario also minimized transportation distances, with a maximum on-site distance of 81 km and an average distance of 27 km. These distances represent a significant reduction compared to single-site (74% decrease) and two-site (34% decrease) scenarios.

The results highlight the potential of utilizing agricultural biomass for biofuel production in Golestān province. Developing a diversified energy portfolio that reduces dependence on fossil fuels and mitigates their environmental impacts necessitates further research in this area.The results highlight the potential of utilizing agricultural biomass for biofuel production in Golestān province. Developing a diversified energy portfolio that reduces dependence on fossil fuels and mitigates their environmental impacts necessitates further research in this area.