Modeling
S. Sharifi; N. Hafezi; M. H. Aghkhani
Abstract
IntroductionEfficient use of energy in paddy production can lower greenhouse gas emissions, safeguard agricultural ecosystems, and promote the growth of sustainable agriculture. Meanwhile, intelligent agriculture has come to the aid of farmers and policy-makers by harnessing cutting-edge technologies, ...
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IntroductionEfficient use of energy in paddy production can lower greenhouse gas emissions, safeguard agricultural ecosystems, and promote the growth of sustainable agriculture. Meanwhile, intelligent agriculture has come to the aid of farmers and policy-makers by harnessing cutting-edge technologies, which will lead to sustainable welfare and the comfort of human society in the present and the future. Therefore, this study aimed to analyze energy consumption and production, as well as model and optimize the yield of two paddy cultivars using Artificial Bee Colony (ABC) and Genetic Algorithms (GA).Materials and MethodsExtensive research data was collected by thoroughly examining documentary and library resources, as well as conducting face-to-face questionnaires with 120 paddy farmers and farm owners in Rezvanshahr city, located in the province of Guilan, Iran, during the 2019-2020 production year. The farms consisted of 80 high-grading and 40 high-yielding paddies. The independent variables were machinery, diesel and gasoline fuels, electricity, seed, compost and straw, biocides, fertilizers, and labor. The dependent variable was paddy yield per hectare [of the farm area]. In the first step, energy consumption and production were calculated by multiplying the variables by their corresponding coefficients. In the second step, all the variables that maximize paddy yield were entered into MATLAB software. An artificial bee colony (ABC) algorithm with a novel and straightforward elitism structure was utilized to enhance the fitness function of the genetic algorithm (GA). The Sphere, Repmat, and Unifrnd functions were employed to determine the objective function, define the position of the bee colony, and quantify the position of the bee colony, respectively. In each generation, 900 new solutions were created, and the algorithm iterated 200 times. For the genetic algorithm, the population was defined as a double vector with a size of 100.Results and DiscussionThe findings revealed that the Hashemi (high-grading) paddy cultivar had an average energy consumption and production of 55.973 and 30.742 GJ·ha-1, respectively. The Jamshidi (high-yielding) paddy cultivar had an average energy consumption of 54.796 GJ·ha-1 and double the energy production of the Hashemi at 62.522 GJ·ha-1. In both cultivars, agricultural machinery consumed the highest amount of energy, while straw consumed the lowest amount. The average energy consumption of tractors in the Hashemi and Jamshidi cultivars was 25.111 and 25.865 GJ·ha-1, respectively, accounting for 44.862% and 47.202% of the total average consumed energy. This undoubtedly demonstrates the significant effect of this input and reflects the operators' skill and experiential knowledge. The evaluation indexes, including R², RMSE, MAPE, and EF, as well as statistical comparisons such as mean, STD, and distribution, consistently demonstrated that the ABC algorithm provided the essential conditions for the fitness function. The results of the bee-genetic algorithm optimization revealed that the majority of the consumed resources could be effectively managed on the farm to closely match optimal conditions. Through this optimization, energy consumption in the Hashemi and Jamshidi cultivars was reduced by 53.96% and 39.41%, respectively.ConclusionGiven its impressive performance and potential for minimizing energy consumption, the ABC-GA algorithm offers an opportunity for policymakers in energy resource management and rice industry managers to develop innovative strategies for significantly reducing energy usage in rice production. This approach could lead to more sustainable and efficient practices in the agricultural sector.
Modeling
M. Boroun; M. Ghahderijani; A. A. Naseri; B. Beheshti
Abstract
IntroductionEnergy analysis offers significant benefits by establishing a foundation for resource conservation, quantifying the energy consumed at each stage of production, identifying processes that require minimal energy input, and supporting sustainable management practices. In sustainable agricultural ...
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IntroductionEnergy analysis offers significant benefits by establishing a foundation for resource conservation, quantifying the energy consumed at each stage of production, identifying processes that require minimal energy input, and supporting sustainable management practices. In sustainable agricultural systems, maximizing the productivity of input energies is a key objective. This study aims to assess energy consumption patterns within the sugar industry and to compare the optimization of energy consumption indicators using two meta-heuristic algorithms, ultimately seeking to enhance resource efficiency and promote sustainable production methods.Materials and MethodsThis study evaluated energy efficiency and environmental impacts in sugarcane-based sugar production at Dehkhoda Sugarcane Agro-Industry Company (in Khuzestan Province, Iran), during the 2019-2020 agricultural cycle. Data collection integrated field questionnaires, expert interviews, operational records from the facility, and national agricultural databases (Ministry of Agriculture Jihad statistics and energy balance sheet). Energy flow were analyzed using MATLAB statistical software and the Equinonet database, with comparative optimization through genetic algorithms and imperialist competitive algorithms to identify efficiency improvements.Results and DiscussionThe results showed that, for the majority of indicators evaluated, the imperialist competitive algorithm outperformed the genetic algorithm in optimizing energy consumption. In addition to reducing the environmental impacts of this profitable industry in the country, it has a high potential for energy savings. The total energy input reduction with the genetic algorithm was 17.05%, while the imperialist competitive algorithm achieved a higher reduction of 26.40%. Natural gas consumption decreased by 3.82% using the genetic algorithm, and by 27.60% with the imperialist competitive algorithm. Direct energy savings were 16.97% for the genetic algorithm and 27.48% for the imperialist competitive algorithm. Soil acidification reduction was 23.03% with the imperialist competitive algorithm and 19.19% with the genetic algorithm, compared to conditions before optimization.ConclusionIn general, it can be concluded that, given the growing demand for sugar production and related industries, as well as the high efficiency of the sugar production sector, it is advisable to utilize expert knowledge and apply meta-heuristics methods to optimize energy consumption and available inputs with the aim of reducing harmful environmental impacts.
Bioenergy
M. Kamali; R. Abdi; A. Rohani; Sh. Abdollahpour; S. Ebrahimi
Abstract
IntroductionSince anaerobic digestion leads to the recovery of energy and nutrients from waste, it is considered the most sustainable method for treating the organic fraction of municipal solid wastes.However, due to the long solid retention time in the anaerobic digestion process, the low performance ...
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IntroductionSince anaerobic digestion leads to the recovery of energy and nutrients from waste, it is considered the most sustainable method for treating the organic fraction of municipal solid wastes.However, due to the long solid retention time in the anaerobic digestion process, the low performance of the process in biogas production as well as the uncertainty related to the safety of digested materials for utilizing in agriculture, applying different pretreatments is recommended.Thermal pretreatment is one of the most common pretreatment methods and has been used successfully on an industrial scale. Very little research, nevertheless, has been done on the effects of different temperatures and durations of thermal pretreatment on the enhancement of anaerobic digestion of the organic fraction of municipal solid wastes (OFMSW). The main effect of thermal pretreatment is the rapturing cell membrane and dissolving organic components. Thermal pretreatment at temperatures above 170 °C may result in the formation of chemical bonds that lead to particle agglomeration and can cause the loss of volatile organic components and thus reduce the potential for methane production from highly biodegradable organic waste. Therefore, since thermal pretreatment at temperatures above 100 °C and high pressure requires more energy and more sophisticated equipment, thermal pretreatment of organic materials at low temperatures has recently attracted more attention. According to the researchers, thermal pretreatment at temperatures below 100 °C did not lead to the decomposition of complex molecules but the destruction of large molecule clots.The main purpose of this study was to find the optimal levels of pretreatment temperature and time and the most appropriate concentration of digestible materials to achieve maximum biogas production using a combination of the Box Behnken Response Surface Method to find the objective function followed by optimizing these variables by Genetic Algorithm.Materials and MethodsIn this study, the synthetic organic fraction of municipal solid waste was prepared similar to the organic waste composition of Karaj compost plant. The digestate from the anaerobic digester available in the Material and Energy Research Institute was used as an inoculum for the digestion process. Some characteristics of the raw materials that are effective in anaerobic digestion including the moisture content, total solids, volatile solids of organic waste, and the inoculum were measured. Experimental digesters were set up according to the model used by MC Leod. After size reduction and homogenization, the synthetic organic wastes were subjected to thermal pretreatment (70, 90, 110 °C) at specific times (30, 90, 150 min).The Response Surface methodology has been used in the design of experiments and process optimization. In this study, three operational parameters including pretreatment temperature, pretreatment time, and concentration of organic material (8, 12, and 16%) were analyzed. After extracting the model for biogas efficiency based on the relevant variables, the levels of these variables that maximize biogas production were determined using a Genetic Algorithm.Results and DiscussionThe Reduced Quadratic model, was used to predict the amount of biogas production. The value of the correlation coefficient between the two sets of real and predicted data was more than 0.95. The results suggested that pretreatment time followed by the pretreatment temperature had the greatest contribution (50.86% and 44.81%, respectively) to biogas production. Changes in the organic matter concentration, on the other hand, did not have a significant effect (p ˂ 0.01) on digestion enhancement (1.63%) but were statistically significant at p ˂ 0.10.The response surface diagram showed that the increase in pretreatment time first led to a rise and then a fall in biogas production. The decline in biogas production seemed set to continue with pretreatment time. Meanwhile, the increase in pretreatment temperature from 70 °C to 110 °C first contributed to higher biogas production and then the decrease in gas production occurred. The reason for this fall was probably the browning and Maillard reaction.The regression model was applied as the objective function for variables optimization using the Genetic Algorithm method. Based on the results of this algorithm, the optimal thermal pretreatment for biogas production was determined at 95 °C for 104 minutes and at the concentration of 12%. The expected amount of biogas production by applying the optimal pretreatment conditions was 445 mL-g-1 VS.ConclusionIn this study, the variables including thermal treatment temperature and time as well as the concentration of organic waste to be anaerobically digested were optimized to achieve the highest biogas production from anaerobic digestion.Statistical analysis of the results revealed that the application of thermal pretreatment increased biogas production considerably. According to the regression model, the contribution of pretreatment time and temperature to biogas production was significant (50.86% and 44.81% respectively). In stark contrast, varying substrate concentrations in the range of 8 to 16% had a smaller effect (1.63%) on biogas production. The results of this study also showed that the best pretreatment temperature and time were 95 °C and 104 minutes, respectively, at a concentration of 12% by generating 445 mL-g-1 VS biogas which is 31.17% higher than the biogas yield from anaerobic digestion of untreated organic wastes at this concentration.
T. Mesri Gundoshmian; F. Keyhani Nasab; Gh. Shahgholi; E. Abdollahi
Abstract
Introduction Today, most of the agricultural machines for doing agricultural operations and covering the entire farm, must move in the farm, and travel a certain distance without doing anything useful. Common agricultural machines are controlled by human beings using habits, machinery models, and personal ...
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Introduction Today, most of the agricultural machines for doing agricultural operations and covering the entire farm, must move in the farm, and travel a certain distance without doing anything useful. Common agricultural machines are controlled by human beings using habits, machinery models, and personal experiences without using computer-based tools. This trend leads to repetitive patterns and affect farm efficincy. Therefore, applying optimization techniques in determining the optimum pattern and track for on-farm machinery would be very effective. One of the main problems of conventional movement patterns on farms is the time wasted on moving towards the end of the field, which will have a big impact on field efficiency. The purpose of this study is to reduce the useless distance traveled by agricultural machines using genetic algorithm while moving on the farm and going from one track to the next, and, consequently, increase farm efficiency. Materials and Methods In this study, the rectangle farm that was 80 meters wide and had an arbitrary length was selected for simulation, and different types of turning methods were tested. The calculations and simulation were based on genetic algorithm using the MATLAB 2013 software. In this case, the minimum traveled distance was set as solution evaluation criterion. The solutions were applied and simulated according to these assumptions: Each gene was considered a track number, and the algorithm’s chromosomes were produced by connecting all the tracks to each other,. The width of each track was considered equal to the width of the machine, and based on reproduction parameters such as population size and the number of repetitions, a percentage of the children were produced through point intersection and another percentage were produced through mutation. In determining the distance between the tracks, Ω or T or U were used for two adjacent tracks, U was used for two tracks that had a track between them, and a longer U was used for tracks that had more than one track between them. Based on the number of the initial population, the chromosomes that were supposed to be parents at the beginning were selected. The children produced new population was created and the above steps were repeated. During the last repetition, the best child chromosome was introduced as the answer. In order to calculate the effects of different methods of turning on the non-working distance covered during agricultural operations, the non-working distance traveled during 5 orders of movement, including 4 traditional orders (continuous, spiral, all-around, and blocked) and 1 smart order were compared to each other. In the continuous pattern, because movement continues in the next track at the end of each track, all the turnings are inevitably done in the Ω way, and thus a greater distance is travelled compared to the U way. In the spiral pattern, the distance travelled in turnings between different tracks on the farm is equal. In the all-around pattern, movements are done from the sides and the operation is concluded at the center of the farm; therefore, the long U method of movement is used at the end of all the tracks, and Ω turning is used for the last track at the center of the farm. In the blocked pattern, the farm is devided into two or more blocks, and the all-around movement pattern is used in each block as an independent farm. In the smart movement pattern, the beginning and ending of the agricultural operations are considered in the vicinity of the hypothetical road which, in addition to facilitating access to the road, had a significant impact on reducing the useless distance traveled on the farm. Results and Discussion The final optimum pattern was compared to traditional patterns in the form of charts. The optimum pattern of movement which uses smart genetic algorithm and avoids long turning methods (such as, Ω and T) leads to reduced wasted time and distance traveled by agricultural machines and increased field efficiency and also, decreasing the non-working traveled distance and waste time approximately, 45 % and 47 % respectively. This is due to avoiding turning methods of Ω and T (compared to the U method). Also, the fatigue resulting from these approaches and their wasted time is greater than U method used in the genetic algorithm pattern. Conclusion The optimum pattern of movement which uses smart genetic algorithm was compared to conventional patterns that showed significant saving in non- working distance and waste time in farm. This optimum pattern can be implemented in automatic navigation but there is the possibility of its implementation by operators in common navigation.
S. F. Mousavi; M. H. Abbaspour-Fard; M. H. Aghkhani; E. Ebrahimi; A. Soheili Mehdizadeh
Abstract
Introduction
The diagnosis of agricultural machinery faults must be performed at an opportune time, in order to fulfill the agricultural operations in a timely manner and to optimize the accuracy and the integrity of a system, proper monitoring and fault diagnosis of the rotating parts is required. ...
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Introduction
The diagnosis of agricultural machinery faults must be performed at an opportune time, in order to fulfill the agricultural operations in a timely manner and to optimize the accuracy and the integrity of a system, proper monitoring and fault diagnosis of the rotating parts is required. With development of fault diagnosis methods of rotating equipment, especially bearing failure, the security, performance and availability of machines has been increasing. In general, fault detection is conducted through a specific procedure which starts with data acquisition and continues with features extraction, and subsequently failure of the machine would be detected. Several practical methods have been introduced for fault detection in rotating parts of machineries. The review of the literature shows that both Artificial Neural Networks (ANN) and Support Vector Machines (SVM) have been used for this purpose. However, the results show that SVM is more effective than Artificial Neural Networks in fault detection of such machineries. In some smart detection systems, incorporating an optimized method such as Genetic Algorithm in the Neural Network model, could improve the fault detection procedure. Consequently, the fault detection performance of neural networks may also be improved by combining with the Genetic Algorithm and hence will be comparable with the performance of the Support Vector Machine. In this study, the so called Genetic Algorithm (GA) method was used to optimize the structure of the Artificial Neural Networks (ANN) for fault detection of the clutch retainer mechanism of Massey Ferguson 285 tractor.
Materials and Methods
The test rig consists of some electro mechanical parts including the clutch retainer mechanism of Massey Ferguson 285 tractor, a supporting shaft, a single-phase electric motor, a loading mechanism to model the load of the tractor clutch and the corresponding power train gears. The data acquisition section consists of a data analyzer (PCA-40), a personal computer, a piezoelectric accelerometer (VMI-102, DT-2234B), a tachometer and two rubber vibration absorbing elements are located between the rig’s components and the plate holder. An evaluation function was employed in order to achieve the optimal structure of neural network models by selecting the number of layers, number of cells in the layers, transfer function, training function, learning functions, performance function, and number of epochs, in such a way that the MSE of the calculated output error was minimal. The data were collected by means of the accelerometer sensor attached on the clutch mechanism, with three different working conditions (normal condition, with worn bearing, and with worn shaft), and three rotational speeds including: 1000 rpm, 1500 rpm and 2000 rpm. The Wavelet Packet Transform (WPT) was applied on the data-set for features vector extraction and the principle component analyses (PCA) was applied for dimension reduction of the features vector. The signal processing and the features extraction are the most important characteristics of the monitoring methodology, by which the working condition of the machine can be determined. These characteristics may be acquired by transforming the signals from the time domain to the frequency domain and MATLAB software is used for this purpose. This software receives the vibration data (time series of output voltage) which are in Excel files format. To remove the noise a suitable filtering procedure was used and finally the statistical parameters of time - frequency were calculated.
Results and Discussion
To verify the accuracy of the Genetic Algorithm model, the required data were collected from the training and testing steps of the Neural Network. For this purpose, the statistical parameters such as mean squared error (MSE), mean absolute error (MAE) and correlation coefficient (r) were used. The optimal parameters of the neural network obtained for the family of Db4. A trial and error procedure was used to minimize the mean square error of the network output and the desired amount of training step. During the training step, four neural networks including Db4, Db30, Db35 and Db40 achieved a gradient descent weight in the learning bias and four neural networks including Db9, Db15, Db20 and Db25 achieved a gradient descent with momentum weight in the learning bias. The two of the achieved neural networks including Db4, Db20 have circular logarithm function and the remaining networks have annular hyperbolic tangent transfer function. The most appropriate networks configuration was acquired when the network exhibited the minimal error with the training and testing data sets. The results show that the highest accuracy of the GA-ANN Artificial neural networks for all rotational speeds (1000, 1500 and 2000 rpm), and working conditions (intact gear and shaft, damaged bearing and worn shaft) observed for the network family of Db4. The highest error observed for the family of Db20 with MSE of 0.011.
Conclusions
Artificial neural networks can somewhat think and make decisions similar to an expert person. In this project in order to predict the occurrence of a failure of the clutch mechanism of MF 285 tractor, the experimental data were obtained using some sensors, and the data were transferred to a computer by means of a data analytical. By training of the neural networks, the errors were identified separately. The output data from the combined Neural Network and Genetic Algorithm shows that the performance of the prediction model is enhanced. Based on the experiments and calculations, the best data set belongs to the family of Db4 network with the least MSE equal to 4.09E-07 and r equal to 0.99999, indicating that the model could precisely detect the faulty bearings or shafts.
M. Ghari; B. Ghamari; N. Bagheri
Abstract
Introduction: Nowadays the number of motor vehicles in large and small cities is growing. Increasing the number of motor vehicles leads to serious increase of the amount of environmental pollution and daily fuel consumption. Motor vehicle emissions that are known as the most air polluting emissions cause ...
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Introduction: Nowadays the number of motor vehicles in large and small cities is growing. Increasing the number of motor vehicles leads to serious increase of the amount of environmental pollution and daily fuel consumption. Motor vehicle emissions that are known as the most air polluting emissions cause 50-90 percent of air pollution. With large increasd in the number of motor vehicles and their emissions todays, many researchers have investigated engine optimization in order to reduce emissions of motor vehicles. But due to the lack of affordable changes in the physical structure of the engine, it is not possible to create major changes in the amount of engine exhaust. Hence, in order to improve engine performance and reduce emissions, a lot of research has been carried out on changes in the fuel and engine inlet air. So, in this study a new method has been proposed and tested in order to detect changes in the charactristics of emissions. So, the effects of enriched nitrous oxide gas on the exhaust emissions of a spark-injection engine were investigated. In this way, a certain amount of Nitrous Oxide (N2O) gas was mixed with the engine inlet air (with concentration of 0, 4, 8, 12 and 16 percent) and it was injected to the engine. Then its effect was studied on emission parameters at various engine rotational speeds. Then, by using genetic algorithm, the optimal values of N2O concentration and engine rotational speed were determined to reach the minimum emission parameters.
Materials and Methods: To measure the engine emission parameters including CO, CO2, HC and NOx, the expriments were conducted after preparing a system to inject inlet air with different percentages of N2O into an Otto engine (model: M13NI). In this study, the randomized complete block design was used to investigate the effect of N2O concentration (five levels) and engine rotational speed (three levels) on exhauste emission parameters. Each expriment was replicated 9 times. For statistical analysis, Duncan’s multiple range test and multivariate analysis of variance were performed by using SPSS Software. Also, each factor was modeled by polynomial equations and the obtained models were optimized in three dimensions by genetic algorithm method in MATLAB Software. After optimization ofeach emission parameter in the same time by multi-objective optimization regression, separately, and determination of the best value of N2O concentration in the inlet air andthe engine rotational speed, the optimizations were compared in order to obtain the minimum value of emission parameters.
Results and discussion: The experimental results indicated that by increasing N2O concentration in the inlet air of motor vehicle engine, the amounts of CO and HC were significantly decreased and the amounts of CO2 and NOx were significantly increased. Also, the results of this study showed that increasing the engine rotational speed at the same time with increasing the N2O concentration caused a significant decrease in the amounts of CO, CO2, HC and NOx. The optimal amount of N2O concentration and engine rotational speed by genetic algorithm method were obtained to be14.545 % and 3184 rpm, respectively.
Conclusions: The main conclusions obtained from this research are listed below:
- The amount of N2O concentration in the engine fuelis the decisive factor for decreasing emissions.
- By increasing N2O concentration in the inlet air of motor vehicle engine, the amounts of CO and HC were significantly decreased and the amounts of CO2 and NOx were significantly increased.
- By increasing the engine rotational speed and N2O concentration, the amounts of CO, CO2, HC and NOx were decreased.
- The optimal amount of N2O concentration and engine rotational speed were obtained to be 14.545 % and 3184 rpm, respectivelyby using the genetic algorithm method. For these values, based on regression models, concentration of CO and CO2, were obtained to be 0.056% and 12.5%, respectively.
- The concentration of N2O and the optimum rotational speed of engine for CO gas were obtained to be 10.562% and 3749 rpm.
- The concentration of N2O and the optimum rotational speed of the engine for CO2 gas were found to be 0% and 2847 rpm, respectively.
- The concentration of N2O and optimum rotational speed of engine for HC werefound to be 12.71% and 3750 rpm, respectively.
- The concentration of N2O and optimum rotational speed of engine for NOx werefound to be 0% and 4300 rpm, respectively.
