Optimum planning of experimental research at the biogas plant

Abstract

Biogas energy, being one of the important components of ecological agricultural production, on the one hand, allows you to conduct successful disposal of crop and livestock waste, receiving organic fertilizers, and on the other hand makes it possible to provide heating and electricity to greenhouses, farms and other industrial buildings and buildings. At the same time, bioenergy technologies require improvement and development, taking into account the specifics of the agro-industrial complex in which they are used. The problems of organizing scientific research at a biogas station are considered. Based on the analysis of the functioning of the biogas plant, a mathematical formulation of the problem of constructing a schedule for conducting experiments taking into account the costs of equipment retooling is formulated. To determine the coefficients of the objective function of the constructed optimization problem, it is proposed to use expert technologies and a fuzzy inference procedure based on a fuzzy production model of knowledge about the subject area. To construct a solution to the optimization problem, along with the classical methods of discrete linear optimization, it is proposed to use evolutionary genetic algorithms that are effective in solving problems of large dimension.

Intellectual assessment of staff sufficiency for innovative development of the sustainable regional agro-industrial complex

“…Then an approach can be used in which the generalized indicator is defined as a weighted sum of particular indicators [11]. For example, when moving from the tier of subcomplexes to the top of the hierarchy, the numerical value of the staffing index of the entire regional agro-industrial complex can be determined by the formula: ind = wfsc indfsc + wnfsc indnfsc wfsc,wnfsc  0, wfsc,wnfsc = 1 (7) where indfsc , indnfsc are values of conditional indicators of staffing of food and non-food subcomplexes, wfsc,wnfsc are weight coefficients reflecting the relative importance of these subcomplexes for the agroeconomy of the region. The values of weighting coefficients determined on the basis of expert judgments (for example, by the method of paired comparisons [11]), in fact (as well as production rules) reflect the knowledge of experts regarding the subject area (agro-industrial sector of the regional economy).…”

“…The enterprises of the agro-industrial complex serve as the economic basis for the development of rural areas [1]. The concept of sustainable development [2] in relation to the agricultural sector of the economy involves the biologization of agriculture (production of environmentally friendly products [3], soil regeneration [4], reduction of agrogenic pollution [5], the use of renewable energy sources [6,7]). One of the important factors hindering the introduction of modern innovative agricultural technologies (along with insufficient scientific study, low economic efficiency and significant risks, as well as technical and technological unavailability) is the lack of qualified personnel.…”

Fuzzy multivariate forecasting of the implementation of regional development programs for the agro-industrial complex

“…Finding the numerical values of the output variables (defuzzification) in accordance with the middle center method is performed using the formula OutVar * =( OutVar *1 P 1 + OutVar *2 P 2 +...+ OutVar *N P N ) / (P 1 +P 2 +...+P N ) (7) where P j = exp(- (x * 1-x *j 1) 2 /σ j 1)…”

“…However, the use of new agricultural production technologies is associated with a number of problems (for example, often, insufficient scientific validity, low economic efficiency, technical and technological unavailability, qualified personnel deficiency, practical experience deficiency, etc. ), which requires constant monitoring, forecasting and (if necessary) corrections of the program progress ( [7]). To manage the implementation of regional programs for the development of the agroindustrial complex, effective digital intellectual (knowledge-oriented) information and analytical support is needed, which is currently underdeveloped.…”

Constructing a smart framework for supplying the biogas energy in green buildings using an integration of response surface methodology, artificial intelligence and petri net modelling

“…Abanades et al (2021) have presented a framework for biogas generation in the globe based on critical review concept [27]. Lomazov et al (2021) have optimized biogas energy production plants with application of fuzzy and genetic algorithm techniques [28]. Stürmer et al (2021) have compared the biogas production based on agricultural wastes in different regions.…”