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<p>This work compares three optimization techniques, namely, SLSQP, PSO, and GA, for the dual objectives of minimizing energy consumption and maximizing biomass yield in the brewery process. The process was modeled as a system of ordinary differential equations (ODEs) describing key process variables, namely, mash temperature, biomass concentration, and substrate concentration, in a 24-h time frame. The optimization results reveal some differences among the three methods. The SLSQP algorithm is way more energy-efficient, with a result of an 18% reduction in energy use from the base case while resulting in a biomass concentration of 0.233&#xa0;g/L. Though the Genetic Algorithm had the highest biomass yield of 0.245&#xa0;g/L, its result also came with a 35% increase in energy use—a fact that makes it undesirable for applications sensitive to energy consumption. PSO presented a balanced performance, with 0.238&#xa0;g/L biomass yield and a moderate energy consumption increase of 10%, between SLSQP and GA. In general, the most adequate choice of method in processes where energy conservation is as relevant as maximizing biomass production will be SLSQP, since this presents the best compromise between energy efficiency and biomass yield. These results show that in a biomass scenario, GA performs very well but always at the cost of high levels of energy input, which cannot be provided by any account from the sustainability point of view. PSO presents a reasonable compromise, as it stands second after SLSQP regarding the achievement of overall energy economy and process optimization. This work presents very useful information on the industrial process to be optimized by multi-objective optimization, particularly in such energy-sensitive industries as fermentation-based ones.</p>

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Development of an Optimization Model for Reducing Energy Utilization and to Increase Biomass Yield in a Brewery Process

  • 期刊论文

  • 发表日期：

    2025年3月5日

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  • Azubuike George Des-wosu,
  • Daniel O. Aikhuele,
  • Harold U. Nwosu

摘要

This work compares three optimization techniques, namely, SLSQP, PSO, and GA, for the dual objectives of minimizing energy consumption and maximizing biomass yield in the brewery process. The process was modeled as a system of ordinary differential equations (ODEs) describing key process variables, namely, mash temperature, biomass concentration, and substrate concentration, in a 24-h time frame. The optimization results reveal some differences among the three methods. The SLSQP algorithm is way more energy-efficient, with a result of an 18% reduction in energy use from the base case while resulting in a biomass concentration of 0.233 g/L. Though the Genetic Algorithm had the highest biomass yield of 0.245 g/L, its result also came with a 35% increase in energy use—a fact that makes it undesirable for applications sensitive to energy consumption. PSO presented a balanced performance, with 0.238 g/L biomass yield and a moderate energy consumption increase of 10%, between SLSQP and GA. In general, the most adequate choice of method in processes where energy conservation is as relevant as maximizing biomass production will be SLSQP, since this presents the best compromise between energy efficiency and biomass yield. These results show that in a biomass scenario, GA performs very well but always at the cost of high levels of energy input, which cannot be provided by any account from the sustainability point of view. PSO presents a reasonable compromise, as it stands second after SLSQP regarding the achievement of overall energy economy and process optimization. This work presents very useful information on the industrial process to be optimized by multi-objective optimization, particularly in such energy-sensitive industries as fermentation-based ones.