Energy modeling with meteorological data and multiobjective optimization of a confectionery stove

摘要: Abstract Confectionery stoving is the process used to produce soft-jelled candies. The has many steps, but most energy-intensive step heating and drying candies, which can consume up 84% of energy at a factory. Drying achieved by ventilating candy stove with large volumes outside air, so impact local climate on confectionery facilities be significant. However, there lack literature investigating process. This work demonstrates how Multiobjective Optimization (MOO) find balanced solution between first cost usage. Furthermore, investigates study whether strategic facility siting in specific zone significantly reduce needs installation costs. paper proposes generic model that uses actual meteorological data product-driven recipe for calculating requirement stove. Four distinct zones were analyzed, it was found operating dry, hot climate, such as Las Vegas, requires 15% less than hot-humid Houston. incorporates Evolutionary Algorithm (MOEA) procedure minimize usage initial cost. algorithm varies airflow rate percentage outdoor air A Pareto Front search defined, compares all solutions against “base case” solution. base case minimum any objective, this work, lowest consuming preferred where small increase consumption, 0.2%, reduces 12%–45%. methods outlined apply energy-consuming manufacturing influenced or broad set functional parameters.