Mechanistic-based probabilistic optimization of industrial wood drying considering energy consumption, process duration, quality and cost

Drying is the most energy consuming process in the industrial transformation of wood. The current energy and climate crisis makes it imperative to adapt the process to energy availability, in terms of quantity, cost and temperature level. Wood drying schedules are historically based on practice. In the present work, a mechanistic drying model is used as a predictive tool to adapt conditions to specific situations. A multiscale computational model, Multi_Wood_DryS, has been combined with a probabilistic optimization code to propose tailor-made drying schedules that meet operators' expectations in terms of energy consumption, quality, drying time and cost. Optimizations of drying schedules of a stack of boards are proposed. The cost-optimized schedule is advantageous for all criteria with a 48% reduction in drying time. The resulting metamodel is a first step toward an intelligent controller for wood drying.