化工过程具有不确定性、非线性、多目标、多约束等特点，其建模是实现生产过程安全、优化操作和先进控制的有效手段。研究不确定环境下的化工过程建模策略已成为工程实际的迫切需要。本项目将深入研究受生物启发的智能优化方法,探索解决具有不确定性的化工过程建模的有效途径。受生物DNA、RNA和蛋白质启发，基于遗传信息表达和分子操作，提出新的单目标和多目标的DNA遗传算法；受膜结构与功能启发，基于进化计算和DNA计算，提出新的单目标和多目标膜计算优化方法；建立化工过程参数模型和非参数模型；采用数值仿真来研究建模策略的适应性；以催化裂化装置和汽油调合过程为背景进行应用研究，考察理论方法的有效性，形成具有普遍指导意义的化工过程建模理论和方法。本项目的研究将为化工过程建模提供新的研究方向和思路，为优化建模的工程应用提供理论依据，具有十分重要的理论意义和应用价值。

Chemical processes have the features of complexity, uncertainty, nonlinearity, multi-objective, multi-constraint, multi-resource coordinated with one another. Process modeling is the key technology to realize the purpose of safety production, optimal operation and advanced control. It is urgent to develop the new modeling strategy of chemical processes under uncertain environments. By going deep into investigate bio-inspired optimization algorithms, this project will explore efficient ways to track the challenging problems of chemical process modeling under uncertainties. Inspired by biological DNA, RNA，protein, and based on genetic information expression and molecular computing, the novel single-objective/ multi-objective DNA genetic algorithms will be proposed. Inspired by membrane structure and functions, and based on evolution computing and DNA computing, the novel single-objective/multi-objective membrane computing based optimization algorithms will be proposed. Both the parameter models and non- parameter models of chemical processes are established under uncertainties. The robustness and flexibility of the process modeling strategies are investigated in detail through numerical simulations. Applications in fluid catalytic cracking processes and gasoline blending processes will demonstrate the efficiency of proposed approaches. The research of this project will provide new research fields and directions for modeling of chemical processes, and offer theoretical basis of engineering applications of optimization modeling. This project has the extremely important theory significance and application value.