组件化软测量软件包的开发与应用

以软测量软件包的研究开发与应用实施为主线,介绍组件化软测量软件包的设计原理、基本框架和特点。进而介绍该软测量软件包在某化纤厂精对苯二甲醛(PTA)生产装置的实施与实际应用效果,放任了软件的有效性和可行性。     

工业渣油催化裂化过程六集总组合反应器模型

工业渣油催化裂化反应主要发生在提升管段和出口的沉降器段的复杂流体动力学区域。通过对工业现场装置流程和过程数据的分析,将发生裂化反应的整个反应器中提升管部分作为活塞流反应器(PFR)和沉降器部分作为全混流反应器(CSTR)的串联组合反应器,并按照渣油催化裂化反应特点建立了简化的6集总组分的串行和并行动力学反应网络模型。所建立的稳态催化裂化反应产率预测模型在数学上表现为提升管部分的微分方程组和沉降器部分的代数方程组。模型设置7个装置因数来校正模型的计算产率与实测产率之间的偏差,并采用工业现场数据回归装置因数。通过对工业装置数据的计算比较,得到的模型产率预测精度很好地满足在线软测量计算要求。     

Multiobjective Optimization of the Industrial Naphtha Catalytic Re-forming Process

1 INTRODUCTION Petroleum refining and petrochemical industries aim at maximizing one prime product while simulta-neously minimizing another accessory product to im-prove the quality of the prime product. Unfortunately, the two requirements are often conflicting or incon-sistent. It is necessary to determine the trade-off com-promises to balance the two objectives[1,2]. As the core of aromatics complex unit, catalytic reforming is a very important process for transforming naphtha into arom…     

催化重整过程的多目标优化

In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reforming process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics reaction network and has been proved to be quite effective in terms of industrial application. The primary objectives include maximization of.yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet temperatures, reaction pressure, and hydrogen-to-oil molar ratio are selected as the decision variables. A genetic algorithm,which is proposed by the authors and named as the neighborhood and archived genetic algorithm (NAGA), is applied to solve this mulfiobjective optimization problem. The relations between each decision variable and the two objectives are also proposed and used for choosing a suitable solution from the obtained Pareto set.     

催化重整过程的多目标优化

In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reforming process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics reaction network and has been proved to be quite effective in terms of industrial application. The primary objectives include maximization of yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet temperatures, reaction pressure, and hydrogen-to-oil molar ratio are selected as the decision variables. A genetic algorithm, which is proposed by the authors and named as the neighborhood and archived genetic algorithm （NAGA）, is applied to solve this multiobjective optimization problem. The relations between each decision variable and the two objectives are also proposed and used for choosing a suitable solution from the obtained Pareto set.