Flowsheet simulations of chemical processes on an industrial scale require the solution of large systems of nonlinear equations,so that solvability becomes a practical issue.Additional constraints from technical,econo...Flowsheet simulations of chemical processes on an industrial scale require the solution of large systems of nonlinear equations,so that solvability becomes a practical issue.Additional constraints from technical,economic,environmental,and safety considerations may further limit the feasible solution space beyond the convergence requirement.A priori,the design variable domains for which a simulation converges and fulfills the imposed constraints are usually unknown and it can become very time-consuming to distinguish feasible from infeasible design variable choices by simply running the simulation for each choice.To support the exploration of the design variable space for such scenarios,an adaptive sampling technique based on machine learning models has recently been proposed.However,that approach only considers the exploration of the convergent domain and ignores additional constraints.In this paper,we present an improvement which particularly takes the fulfillment of constraints into account.We successfully apply the proposed algorithm to a toy example in up to 20 dimensions and to an industrially relevant flowsheet simulation.展开更多
Base on industrial research and experience, the process of methanol distillation is analyzed,and above all, a new concept of high pressure flowsheet and low pressure flowsheet is defined. The new configuration helps t...Base on industrial research and experience, the process of methanol distillation is analyzed,and above all, a new concept of high pressure flowsheet and low pressure flowsheet is defined. The new configuration helps to handle problems encountered in many factories in China. The inter influence between process and column internal pattern is also pointed out. Recommendation of new column internal designs is given. Finally, industrial examples tell the how the new concept works and the possibility of combining process to give more opens to solve engineering problems.展开更多
基金This work was developed within the Fraunhofer Cluster of Excellence“Cognitive Internet Technologies”.
文摘Flowsheet simulations of chemical processes on an industrial scale require the solution of large systems of nonlinear equations,so that solvability becomes a practical issue.Additional constraints from technical,economic,environmental,and safety considerations may further limit the feasible solution space beyond the convergence requirement.A priori,the design variable domains for which a simulation converges and fulfills the imposed constraints are usually unknown and it can become very time-consuming to distinguish feasible from infeasible design variable choices by simply running the simulation for each choice.To support the exploration of the design variable space for such scenarios,an adaptive sampling technique based on machine learning models has recently been proposed.However,that approach only considers the exploration of the convergent domain and ignores additional constraints.In this paper,we present an improvement which particularly takes the fulfillment of constraints into account.We successfully apply the proposed algorithm to a toy example in up to 20 dimensions and to an industrially relevant flowsheet simulation.
文摘Base on industrial research and experience, the process of methanol distillation is analyzed,and above all, a new concept of high pressure flowsheet and low pressure flowsheet is defined. The new configuration helps to handle problems encountered in many factories in China. The inter influence between process and column internal pattern is also pointed out. Recommendation of new column internal designs is given. Finally, industrial examples tell the how the new concept works and the possibility of combining process to give more opens to solve engineering problems.
