Coal gasificationtechnology plays a pivotal role in chemical production as a key process for efficiently converting coal into liquid fuels and chemical feedstocks.During gasification,high-temperature reactions generat...Coal gasificationtechnology plays a pivotal role in chemical production as a key process for efficiently converting coal into liquid fuels and chemical feedstocks.During gasification,high-temperature reactions generate syngas,and optimizing its operational parameters is essential for improving syngas quality,carbon efficiencyand liquid fuel yield.However,the intricate chemical reactions and heat transfer mechanisms in gasificationnecessitate costly simulations or experimental testing,making it an expensive multi-objective optimization problem.To address this challenge,this paper proposes a Knee Point-guided Heterogeneous Surrogate-assisted Evolutionary Algorithm(KG-HSEA)that integrates Kriging and Feedforward Neural Networks(FNN)to construct a heterogeneous surrogate model,leveraging their complementary strengths to reduce computational costs while maintaining predictive accuracy.By incorporating a knee point-guided search mechanism,the method prioritizes solutions that embody critical trade-offs among conflictingobjectives.Moreover,an adaptive sampling strategy combined with dual-archive management is employed to dynamically update the surrogate model,ensuring it adapts to unstable operating conditions while maintaining robust convergence-diversity balance in coal gasificationprocesses.Experimental results show that KG-HSEA achieved a 71.9% superiority rate with 23 optimal solutions out of 32 benchmark problems,highlighting its potential for efficientand feasible coal gasificationoptimization.展开更多
基金supported by Scientific and Technological Innovation 2030-"New Generation ArtificialIntelligence"Major Project(2021ZD0112301)Science Fund for Creative Research Groups of the National Natural Science(62021003)+1 种基金the National Natural Science Foundation of China(62303027)The open project of China Food Flavor and Nutrition Health Innovation Center(CFC2023B-021).
文摘Coal gasificationtechnology plays a pivotal role in chemical production as a key process for efficiently converting coal into liquid fuels and chemical feedstocks.During gasification,high-temperature reactions generate syngas,and optimizing its operational parameters is essential for improving syngas quality,carbon efficiencyand liquid fuel yield.However,the intricate chemical reactions and heat transfer mechanisms in gasificationnecessitate costly simulations or experimental testing,making it an expensive multi-objective optimization problem.To address this challenge,this paper proposes a Knee Point-guided Heterogeneous Surrogate-assisted Evolutionary Algorithm(KG-HSEA)that integrates Kriging and Feedforward Neural Networks(FNN)to construct a heterogeneous surrogate model,leveraging their complementary strengths to reduce computational costs while maintaining predictive accuracy.By incorporating a knee point-guided search mechanism,the method prioritizes solutions that embody critical trade-offs among conflictingobjectives.Moreover,an adaptive sampling strategy combined with dual-archive management is employed to dynamically update the surrogate model,ensuring it adapts to unstable operating conditions while maintaining robust convergence-diversity balance in coal gasificationprocesses.Experimental results show that KG-HSEA achieved a 71.9% superiority rate with 23 optimal solutions out of 32 benchmark problems,highlighting its potential for efficientand feasible coal gasificationoptimization.
