Counterfactual Analysis of the Efficiency of Decontamination of Livestock Production Organic Wastes

IF 0.1 Q4 ENGINEERING, MULTIDISCIPLINARY Engineering Technologies and Systems Pub Date : 2023-12-29 DOI:10.15507/2658-4123.033.202304.466-489
Yakov P. Lobachevsky, A.V. Shemyakin, N. Limarenko, I. Uspensky, I. Yukhin
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Abstract

Introduction. The implementation of the decree of the President of the Russian Federation is aimed at ensuring the food security of the country and requires the industrialization of the agro-industrial sector. The effectiveness of industrialization depends on the use of automated, intelligent solutions at all stages of implementing technological processes. Livestock is an agro-industrial sector generating the largest amount of organic waste materials, which are potential energy carriers: litter, liquid manure, process effluents, etc. According to the data from the Russian Statistics Committee and the research results, the annual volume of manure generated from farms is from 43.3 to 45.1 million tons, while there is an upward trend. The used energy potential from the entire volume does not exceed 40%. It is possible to increase the efficiency of using the energy potential of organic animal waste materials through implementing digitalized solutions. A strategic tool for the effective industrialization of livestock is the implementation of application software products that ensure the growth of ecological and energy effects. Aim of the Article. The aim of the study is a counterfactual evaluation of the efficiency of the model for decontaminating liquid pig manure in the decontamination activator. Materials and Methods. Counterfactual analysis is a tool for formalizing complex, multifactorial processes to ensure their subsequent digitalization. The essence of the analysis consists in a “surveyˮ of the analyzed model through which the values of variables are determined providing changes that lead to a deviation of the response beyond the boundary conditions during interpretation. The advantage of counterfactual analysis is the stability and transparency of the model to external influences during machine learning. It is known that the representative pathogenic markers of the decontamination efficiency of liquid pig manure are helminth eggs and the number of colony-forming units of common coliform bacteria (CFU CCB). However, for testing and implementing an algorithm for counterfactual analysis of a mathematical model, it is acceptable to use the number of CFU CCB. The object of the study was liquid pig manure with a humidity from 88% to 98%, the subject was a counterfactual analysis of the dependence of the number of CFU CCB on the exposure time in the activator, the concentration of active chlorine, the mass of working bodies, magnetic induction, and liquid manure humidity. Results. The results of counterfactual evaluation and analysis carried with the use of the Python programming language and the PyCharm 2022.2 environment are presented in the tables. The counterfactual evaluation made it possible to identify ranges of variation of factors, the use of which can represent the potential of boundary conditions in solving the optimization problem. The cells of these values are highlighted in grey-blue. The most preferred ranges based on counterfactual evaluation are in the cells highlighted in green. Discussion and Conclusions. There has been substantiated the prospects of using active chlorine in combination with the influence of ferromagnetic working bodies moving in an alternating rotating electromagnetic field as a decontamination activator. On the basis of counterfactual evaluation it was established that the most significant factors for determining the efficiency of decontamination of liquid pig manure by the number of CFU CCB are: magnetic induction in the working zone of the activator inductor, active chlorine concentration and exposure time.
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畜牧生产有机废物净化效率的反事实分析
导言。执行俄罗斯联邦总统令的目的是确保国家粮食安全,这就要求农工部门实现工业化。工业化的有效性取决于在实施技术流程的各个阶段使用自动化、智能化解决方案。畜牧业是产生有机废料最多的农工业部门,这些废料是潜在的能源载体:垃圾、液态粪便、工艺废水等。根据俄罗斯统计委员会的数据和研究成果,农场每年产生的粪便量为 4,330 万吨至 4,510 万吨,并呈上升趋势。全部粪便的能源使用潜力不超过 40%。通过实施数字化解决方案,有可能提高有机动物废料能源潜力的利用效率。有效实现畜牧业产业化的战略工具是实施确保生态和能源效应增长的应用软件产品。文章的目的。研究的目的是对去污活化剂中的液态猪粪去污模型的效率进行反事实评估。材料和方法。反事实分析是一种工具,用于将复杂的多因素过程正规化,以确保其随后的数字化。分析的本质在于对分析模型进行 "调查ˮ",通过调查确定变量值,从而在解释过程中提供导致响应偏离边界条件的变化。反事实分析的优势在于模型的稳定性和在机器学习过程中对外部影响的透明度。众所周知,液态猪粪净化效率的代表性病原体指标是蠕虫卵和普通大肠菌群的菌落形成单位数(CFU CCB)。不过,为了测试和实施数学模型的反事实分析算法,使用 CFU CCB 的数量是可以接受的。研究对象是湿度在 88% 至 98% 之间的液态猪粪,主题是反事实分析 CFU CCB 的数量对活化剂暴露时间、活性氯浓度、工作体质量、磁感应和液态粪便湿度的依赖性。结果使用 Python 编程语言和 PyCharm 2022.2 环境进行的反事实评估和分析结果列于表中。通过反事实评估,可以确定各因素的变化范围,利用这些范围可以在解决优化问题时体现边界条件的潜力。这些值的单元格以灰蓝色标出。根据反事实评估得出的最优选范围位于绿色区域。讨论与结论。在交变旋转电磁场中移动的铁磁性工作体的影响下使用活性氯作为去污活化剂的前景已经得到证实。在反事实评估的基础上,确定了决定液态猪粪净化效率(CFU CCB 数量)的最重要因素是:活化剂感应器工作区的磁感应、活性氯浓度和暴露时间。
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来源期刊
Engineering Technologies and Systems
Engineering Technologies and Systems ENGINEERING, MULTIDISCIPLINARY-
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33.30%
发文量
29
审稿时长
12 weeks
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