Comparison of Energy Consumption Optimization in Sugar Factory Using Meta-Heuristic Algorithms

Introduction Energy 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 Methods This 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 Discussion The 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. Conclusion In 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.