Modeling
M. A. Hormozi; H. Zaki Dizaji; H. Bahrami; N. Monjezi
Abstract
IntroductionThe development of mechanization and machine technology can have positive and negative effects on the economic, social, and environmental conditions of a region. Conflicts in these areas complicate the selection and optimization of sustainable mechanization systems. One of the basic questions ...
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IntroductionThe development of mechanization and machine technology can have positive and negative effects on the economic, social, and environmental conditions of a region. Conflicts in these areas complicate the selection and optimization of sustainable mechanization systems. One of the basic questions in the selection of a sustainable agricultural mechanization system is how and with what methodology would it be possible to propose the closest mechanization model that will overcome the simultaneous contradictions between the three pillars of sustainability; taking into account the natural and technical limitations in agricultural production. What is the appropriate approach considering the economic, environmental, and social aspects? The current research aims to provide a framework for an optimal mechanization model to achieve the goals of agricultural sustainability so that it can be implemented and applied practically. It is possible to provide a model that addresses the conflicting economic, social, and environmental aspects by quantitatively optimizing the level of mechanization.Materials and Methods In this study, a framework is applied whereby contradictory goals of agricultural sustainability can be achieved simultaneously. After selecting the indices and data collection, by combining Shannon entropy and TOPSIS, the similarity index was obtained for each objective. The similarity indices and values of the Benefit-Cost Ratio calculated for each system were considered as coefficients of three objective (economic, social, and environmental) functions in multi-objective optimization. The multi-objective optimization model was applied to achieve sustainable mechanization patterns and was solved using the NSGA-II algorithm. For framework validation, paddy production mechanization systems in the Ramhormoz region located in southwestern Iran were analyzed with constraints: land, water, and machinery. The five mechanization systems of paddy production included puddled transplanted, un-puddled transplanted, water seeded, dry seeded, and, no-till.Results and DiscussionPareto-optimal solutions of different scenarios with water and machine constraints showed that this framework cannot only meet the sustainable goals, but also the optimal allocation of mechanization systems is identified and the effect of different scenarios under different constraints can be examined. The sustainability goals between the no-tillage and planting with puddling systems are highly contradictory. The no-tillage system has the highest score in the environmental aspect and the lowest score in the social and economic aspects. This modern system was developed in Ramhormoz three years ago and has faced technical, economic, and social challenges ever since. The cultivated area using this system was 43 hectares in 2019. Despite the speed and ease of planting with this system, and its direct environmental benefits, the possibility of fungal outbreaks is raised due to the presence of wheat residues from previous cultivation and the warm and humid environment of cultivation. Additionally, weed outbreaks caused by periodic irrigation have greatly affected the satisfaction and profitability of this system, leading to the highest amount of pesticides consumed among the studied systems. The results of multi-objective optimization of sustainable rice mechanization systems in Ramhormoz city showed that the total surface area of optimal point systems is in the range of 2700 to 3200 hectares, which is close to the area under rice cultivation in Ramhormoz (3310 hectares) and it indicates that the output of the model is according to the applied restrictions and close to reality. The limitation of machinery and water has made the two planting systems of un-puddled transplanting and dry-seeding better than other systems. Removing only the machinery restriction can lead to an increase in the area under rice cultivation by about 700 hectares. This means that the requirement for the development of sustainable rice cultivation in Ramhormoz is to strengthen and support modern mechanized systems of no-tillage, dry-seeding, and planting with puddling, with a focus on systems with less water consumption which are the systems with higher levels of mechanization. Without water limitation, if the model is subject to the current machinery limitations, the optimal mechanization systems are the more traditional ones such as transplanting without puddling and wet-seeding.ConclusionOne of the most fundamental challenges in the development of mechanization is identifying systems that can best balance the economic, social, and environmental aspects of sustainability and minimize environmental damage whilst maximizing economic and social benefits. Using the framework for sustainable mechanization will not only accomplish sustainable goals in identifying the optimum agricultural mechanization level, but it will also allow researchers and implementers in the agricultural sector to examine the outcome of various scenarios under different constraints. This framework can be used to find the optimal model for mechanization of all stages of tillage, planting, harvesting, and post-harvest in diverse geographical areas.
P. Najafi; M. A. Asoodar; A. Marzban; M. A. Hormozi
Abstract
Introduction: The performance of agricultural machines depends on the reliability of the equipment used, the maintenance efficiency, the operation process, the technical expertise of workers, etc. As the size and complexity of agricultural equipment continue to increase, the implications of equipment ...
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Introduction: The performance of agricultural machines depends on the reliability of the equipment used, the maintenance efficiency, the operation process, the technical expertise of workers, etc. As the size and complexity of agricultural equipment continue to increase, the implications of equipment failure become even more critical. Machine failure probability is (1-R) and R is machine reliability (Vafaei et al., 2010). Moreover, system reliability is the probability that an item will perform a required function without failure under stated conditions for a stated period of time (Billinton and Allan, 1992). Therefore, we must be able to create an appropriate compromise between maintenance methods and acceptable reliability levels. Precision failure data gathering in a farm is a worthwhile work, because these can represent a good estimate of machine reliability combining the effects of machine loading, surrounding effects and incorrect repair and maintenance. Each machine based on its work conditions, parts combinationand manufacturing process follows a failures distribution function depending on the environment where the machine work and the machine’s specifications (Meeker and Escobar, 1998). General failures distributions for contiguous data are normal, log-normal, exponential and Weibull (Shirmohamadi, 2002). Each machine can represent proportionate behavior with these functions in short or long time.
Materials and methods: The study area was the Hakim Farabi agro-industry Company located 35 kilometers south of Ahvaz in Iran. Arable lands of this company are located in 31 to 31°10 N latitude and 45 to 48°36 E longitudes. The region has dry and warm climate. A total of 24 Austoft 7000 sugarcane chopper harvester are being used in the company. Cane harvesters were divided into 3 group consisting of old, middle aged and new. From each group, one machine was chosen. Data from maintenance reports of harvesters which have been recorded within 400 hours were used. Usually, two methods are usedfor machine reliability modeling. The first is Pareto analysis and the second is statistical modeling of failure distributions (Barabadi and Kumar, 2007). For failures distribution modeling data need to be found, that are independent and identically (iid) distributed or not. For this, trend test and serial correlation tests are used. If the data has a trend, those are not iid and its parameters are computed from the power law process. For the data that does not havea trend, serial correlation testare performed. If the correlation coefficient is less than 0.05 the data is not iid. Therefore, its parameters reach via branching poison process or other similar methods; if the correlation coefficient is more than 0.05, the data are iid. Therefore, the classical statistical methods will be used for reliability modeling. Trend test results are compared with statistical parameter.
A test for serial correlation was also done by plotting the ith TBF against the (i-1)th TBF, i ¼ 1; 2; . . . ; n: If the plotted points are randomly scattered without any pattern, it can be interpreted that there is no correlation in general among the TBFs data and the data is independent. To continue, one must choose as the best fit distribution for TBF data. Few tests can be used for best fit distribution that include chi squared test and Kolmogorov–Smirnov (K-S) test. Chi squared test is not valid when the data are less than 50. Therefore, when the TBF data are less than 50, K-S test must be used. Hence, the K-S test can be used for each TBF data numbers. When the failure distribution has been determined, the reliability model may be computed by equation (2).Results and discussion: Results of trend analysis for TBF data of sugarcane harvester machines showed that the calculated statistics U for all machines was more than chi squared value that was extracted fromthe chi square table with 2 (n-1) degrees of freedom and 5 percent level of significance. Hence, it is possible that all of the machines’ TBF data will have identically and independent distributions. For validating this hypothesis, correlation testwas performed on TBF data that verified prior results. Then, Kolmogorov- Simonov test was done on TBF data. Results showed that all three machines followed Weibull 3 parameters function, but the shape parameter was different for them. The analysis showed the shape parameter for old, middle aged and new cane harvesters was 1.5, 1.42 and 1.35, respectively.
Conclusions: In order to control and reduce failures and to plan and schedule the harvester operations in optimum time, machine reliability must be known. In this paper, three sugarcane harvesters were studied individually. From the trend analysis and serial correlation, it is seen thatthe assumption of identically and being independently distributed was valid for all machines’ TBF data of sugarcane chopper harvesters.
