Modeling
M. Sami; A. Akram; M. Sharifi
Abstract
IntroductionThe need to develop alternative energy sources especially renewable energy has become increasingly apparent with the incident of fuel shortages and escalating energy prices in recent years. With the advent of renewable energy, various studies have been conducted to investigate the potential ...
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IntroductionThe need to develop alternative energy sources especially renewable energy has become increasingly apparent with the incident of fuel shortages and escalating energy prices in recent years. With the advent of renewable energy, various studies have been conducted to investigate the potential of biogas production from agricultural waste. Considering the importance of retention time and methane production potential for designing industrial digesters, many studies on potential analysis and modeling of the digestion process of different products have been carried out by various researchers. These studies are valuable for the design and implementation of anaerobic digesters. Apple is one of the most popular fruits in many parts of the world and is widely cultivated in many temperate regions of the world. Considering the large volume of apple waste in Iran, this study was designed based on potential evaluation and modeling of biogas production from apple pulp.Materials and MethodsIn order to measure the potential of biogas production from apple pomace, a number of lab-scale digesters with a capacity of 600 ml and a working capacity of 400-500 ml were made. pH and C/N ratio were modified by adding NaOH and urea solution, respectively. Three different temperature treatments including psychrophilic (ambient temperature), mesophilic (37ºC), and thermophilic (47ºC) were applied to the substrate. Used pomace samples were collected from the output of an apple juice factory in southern Isfahan province, Iran. Anaerobic Biodegradability (ABD) was obtained by dividing the experimental methane production potential (BMP) obtained from the experimental results on the theoretical methane production potential. Three most common kinetic models of Gompertz, Logistic, and Richards were used to predict and stimulate the cumulative methane production of treatments.Results and DiscussionUnder ambient temperature, the digestive process took a longer time, and the time of maximum dilly biogas production was considerably more than the other two treatments. Statistically, production time and peak time of this treatment was higher than the other two treatments at 1% significance level. Maximum daily biogas production in the ambient treatment was observed on day 37th with a volume of 6.99 g-VS-1 ml, while maximum daily biogas production in the treatments of 37 °C and 47 °C were observed on days 22th (20.16 ml g-VS-1) and 20th (25.57 ml g-VS-1), respectively. In all three treatments, daily biogas production increased sharply in the first incubation days and after that reduced and then production increased again. In mesophilic and thermophilic treatments, the production of biogas modestly stopped after 35 days, but under the ambient temperature, the process of production continued after 55 days. The methane concentration of biogas in the psychrophilic treatment was significantly lower than the other two treatments at 1% level. Two treatments of 37°C and 45°C have a significant difference in methane yield at 1% level. Nevertheless, the production of biogas in two treatments was not statistically different. In all three treatments, the lowest pH was recorded after 7 days of production and the highest pH was recorded on days 34-40. All three kinetic equations were able to simulate the methane production process with high precision, although the results of the Logistic model provided higher accuracy. In the treatment 47 °C, the efficiency of the studied equations was higher than other treatments and models were able to predict the production process with higher accuracy. Results of the experiment show the high biochemical methane production potential of apple pomace (473.17 ml g-VS-1), which under laboratory condition of this study up to 63.9% of this potential (302.70 ml g-VS-1) was obtained. ConclusionThis study results are valuable for the design and implementation of industrial digesters. The results indicate the apple pomace has a high potential for the production of methane and its biodegradability is high. Apart from pH that is acidic, other apple pulp factors are appropriate for the activity of methanogenic bacteria. In terms of nutrients, apple pomace is also a good environment for the growth of anaerobic bacteria.
A. Kaab; M. Sharifi; H. Moradi
Abstract
IntroductionCantaloupe is a one-year-old herb of gourds and edible fruit with very good properties. Cantaloupe is one of the best sources of vitamin A and is rich in beta carotene, which is converted into vitamin A in the body. In addition, it contains other useful nutrients such as potassium, steel, ...
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IntroductionCantaloupe is a one-year-old herb of gourds and edible fruit with very good properties. Cantaloupe is one of the best sources of vitamin A and is rich in beta carotene, which is converted into vitamin A in the body. In addition, it contains other useful nutrients such as potassium, steel, fiber, magnesium, iodine and vitamins B5, B3, B6 and B1. Life cycle assessment in recent years has become an appropriate tool for assessing environmental impacts in agricultural and food industries. The purpose of this study was to evaluate the life cycle assessment of this horticultural crop in terms of energy consumption and the environmental impacts in the city of Iwan West, Ilam province.Materials and MethodsThe data were collected from dryland cantaloupe producers in the city of Iwan West, Ilam province using questionnaires and interviews were collected from farmers. In this study, four important energy indices were energy use efficiency (EUE), energy productivity (EP), specific energy (SE), and net energy gain (NEG). Environmental impacts on dryland cantaloupe production were evaluated using a life cycle assessment approach and the obtained indexes were calculated using the CML 2 baseline 2000 model. Ecoinvent databases were used to access needed information and data analysis was done with Simapro software. In a life cycle assessment project, all production processes of a product from the stage of extraction of materials to disposal of the remaining waste from the product are reviewed and the results of the reduction of environmental degradation are applied. Each life cycle assessment project has four essential steps including, goal and scope definition, life cycle inventory, environmental impact assessment, and interpretation.Results and DiscussionInput and output energy analysis in dryland cantaloupe productionThe total input and output energies for dryland cantaloupe were calculated to be 39021.59 and 39190.43 MJ ha-1, respectively. Diesel fuel, agricultural machinery and nitrogen fertilizers were the most widely used energy inputs with 51%, 24%, and 14%, respectively. Energy use efficiency for dryland cantaloupe production was calculated at 1.004.Analysis of environmental impacts in dryland cantaloupe productionIn this study, the global warming potential per produced product in dryland cantaloupe production was estimated to be equal to 202.45 kgCO2 eq. from among inputs, diesel fuel had the most impact on the effects of abiotic depletion and ozone layer depletion, and in all parts of the effects of agricultural machinery and nitrogen fertilizers, the largest share of pollutants was allocated. The results of normalization showed that the effect of marine aquatic ecotoxicity and freshwater aquatic has the highest environmental burden on dryland cantaloupe production.ConclusionThe results of energy analysis showed that the total energy inputs were equal to 39021.59 MJ ha-1. Among inputs of diesel fuel, agricultural machinery, and nitrogen fertilizer were the most consumed energy inputs. The energy use efficiency index and the net energy in this study were 1.004 and 168.84 MJ ha-1, respectively. The results of environmental impacts had shown that diesel fuel, nitrogen fertilizer, and agricultural machinery had been most affected. It is recommended that proper management of agricultural machinery, equipping fields with new and suitable machines and avoiding the use of tractors and worn-out tools should be put in order to minimize the energy consumption and environmental pollutants generated by the production. Less use of chemical fertilizers (especially nitrogen) and its replacement with organic fertilizers can also be affected.
O. Ghaderpour; Sh. Rafiee; M. Sharifi
Abstract
Introduction Agricultural productions has been identified as a major contributor to atmospheric greenhouse gases (GHG) on a global scale with about 14% of global net CO2 emissions coming from agriculture. Identification and assessment of environmental impact in the production system will be leading to ...
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Introduction Agricultural productions has been identified as a major contributor to atmospheric greenhouse gases (GHG) on a global scale with about 14% of global net CO2 emissions coming from agriculture. Identification and assessment of environmental impact in the production system will be leading to achieve the goals of sustainable development, which would be achieved by life cycle assessment. To find the relationship between inputs and outputs of a production process, artificial intelligence (AI) has drawn more attention rather than mathematical models to find the relationships between input and output variables by training, and produce results without any prior assumptions. The aims of this study were to life cycle assessment (LCA) of Alfalfa production flow and prediction of GWP (global warming potential) per ha produced alfalfa (kg CO2 eq.(ha alfalfa)-1) with respect to inputs using ANFIS. Materials and Methods The sample size was calculated by using the Cochran method, to be equals 75, then the data were collected from 75 alfalfa farms in Bukan Township in Western Azerbaijan province using face to face questionnaire method. Functional unit and system boundary were determined one hectare of alfalfa and the farm gate, respectively. Inventory data in this study was three parts, included: consumed inputs in the alfalfa production, farm direct emissions from crop production and indirect emissions related to inputs processing stage. Direct Emissions from alfalfa cultivation include emissions to air, water and soil from the field. Data for the production of used inputs and calculation of direct emission were taken from the EcoInvent®3.0 database available in simapro8.2.3.0 software and World Food LCA Database (WFLD). Primary data along with calculated direct emissions were imported into and analyzed with the SimaPro8.2.3.0 software. The impact-evaluation method used was the CML-IA baseline V3.02 / World 2000. Damage assessment is a relatively new step in impact assessment. The purpose of damage assessment is to combine a number of impact category indicators into a damage category (also called area of protection). To assess the damage in this study, IMPACT 2002+ V2.12 / IMPACT 2002+ method was used. ANFIS is a multilayer feed-forward network which is applying to map an input space to an output space using a combination of neural network learning algorithms and fuzzy reasoning. In order to enable a system to deal with cognitive uncertainties in a manner more like humans, neural networks have been engaged with fuzzy logic, creating a new terminology called ‘‘neuro-fuzzy method. An ANFIS is used to map input characteristics to input membership functions (MFs), input MF to a set of if-then rules, rules to a set of output characteristics, output characteristics to output MFs, and the output MFs to a single valued output or a decision associated with the output. The main restriction of the ANFIS model is related to the number of input variables. If ANFIS inputs exceed five, the computational time and rule numbers will increase, so ANFIS will not be able to model output with respect to inputs. In this study, the number of inputs were ten, including machinery, diesel fuel, nitrogen, phosphate, electricity, water for irrigation, labor, pesticides, Manure and seed and GWP was as the model output signal. To solve this problem and employ all input variables, we proposed clustering input parameters to four groups. Correspondingly, the proposed model was developed using seven ANFIS sub-networks. To obtain the best results several modifications were made in the structure of ANFIS networks, and some parameters were calculated to compare the results of different models. Making a comparison between different topologies the employment of some indicators was a pivotal to get a good vision of various the structures, such as the correlation coefficient (R), Mean Square Error (MSE) and Root Mean Square Error (RMSE). In addition, for checking comparison between experimental and modeled data, the t-test was performed. The null hypothesis was equality of data average. To develop ANFIS models, MATLAB software (R2015a) was used. Results and Discussion Impact categories including Global warming potential (GWP), eutrophication potential (EP), human toxicity potential (HTP), terrestrial ecotoxicity potential (TEP), oxidant formation potential (OFP), acidification potential (AP), Abiotic depletion (AD) and Abiotic depletion (fossil fuels) were calculated as 13373 kg CO2 eq, 19.78 kg PO4-2 eq, 2054 kg 1,4-DCB eq, 38.7 kg 1,4-DCB eq, 3.84 kg Ethylene eq, 90.64 kg SO2 eq, 0.015 kg Sb eq and 205169 MJ, respectively. The results of damage assessment of alfalfa production revealed that electricity in three categories, human health damage, climate change and ecosystem quality had maximum role, but in the resources damage category was the largest share of damage related direct emissions. The value of the climate change was calculated as 13373 kg CO2 eq. The best structure was including five ANFIS network in the first layer, two network in the second layer and a network in output layer. Values of R, MSE and RMSE for the final ANFIS in k-fold model were 0.983, 0.107 and 0.327 and in C-means model were 0.999, 0.007 and 0.082, respectively. The p-value in t-test was 0.9987 that indicates non-significant difference between the mean of modeling and experimental data. Coefficient of determination (R2) between actual and predicted GWP based on the best k-fold and C-means models were 0.994 and 0.99, respectively. The coefficient of determination for these index demonstrated the suitability of the developed network for prediction of GWP of alfalfa production in the studied area. Conclusion Based on the results of this study, to reduce the emissions, electricity consumption should be reduced. Adapting of electro pumps power with the well depth and the amount of required water taken for field will be a possible solution to reduce the use of electricity in order to trigger of electro pumps and thus reducing of emissions related to it. In some situations, the type of mineral fertilizer is the main determinant of emissions at the whole farm level and changing the type of fertilizer could significantly reduce the environmental impact. Comparison of GWP modeling results using two methods of k-fold and C-means revealed that C-means method has higher accuracy in prediction of GWP. Also the high quantities for the determination coefficient related to both modeling methods demonstrates high correlation between actual and predicted data.
M. Sharifi; A. Akram; Sh. Rafiee; M. Sabzehparvar
Abstract
Alborz province with an area of about 5121.7 km2 has about 0.31% of the total area of the country. The total arable area of the province is about 48954 hectares. Water, land and capital are the most important factors for agricultural production. By understanding the subjective beliefs, decision-making ...
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Alborz province with an area of about 5121.7 km2 has about 0.31% of the total area of the country. The total arable area of the province is about 48954 hectares. Water, land and capital are the most important factors for agricultural production. By understanding the subjective beliefs, decision-making criteria and economic incentives of local farmers, the priority of crops can be achieved with the maximum profitability of farmers and the least damage to the resources (water and land). The combination of Fuzzy Delphi techniques and methods of integrating analytical hierarchy process (AHP) can be an appropriate approach for achieving this goal. By employing the above combination of Fuzzy and AHP techniques, the priorities of the strategic agricultural crops in Alborz province achieved as wheat, barley, corn silage, alfalfa, cotton and canola, with final priority weighting factors of 0.496, 0.403, 0.354, 0.320, 0.183, and 0.090, respectively. By comparing the decision criteria it has been determined that the farmers prefer the amount of cultivation area, net income, production costs and livestock needs with the relative importance factors of 0.487, 0.410, 0.346 and 0.188, respectively. Among all prioritization criteria, the cultivated area had the highest priority. Water shortage, labor costs, lack of financial support, and governmental purchase allowance for wheat, were the main reasons for shifting the cultivated area towards wheat cultivation with total area of 14350 hectares.
