乙烯蒸汽裂解原料优化

目前,我国乙烯工业已经进入了第三次建设、扩能改造时期。全国正在运行的18套乙烯装置中,部分装置已经扩能改造完毕,还有部分装置正在进行当中或正在筹备当中。截止2001年底,我国乙烯生产能力已接近5000kt/a,但是也无法满足不断增长的社会需求。“十五”期间我国要投巨资建设4个大型乙烯项目,扬子600kt/a项目、上海900kt/a项目和南海800kt/a项目已经开工建设,福建600kt/a项目已经草签了建设合同,预计到2010年我国乙烯生产能力可达到10000kt/a。上述项目都是与国外大公司进行的合作,因此我们的管理者和工程技术人员迫切需要更新知识,学习先进技术和先进管理经验。就是在这种大的背景下,中国石化集团公司在石化管理干部学院举办了第一届乙烯技术高级研修班。本刊从本期开始设立“专家讲座”栏目,连载特邀授课专家中国石化上海石化公司原副总经理瞿国华论“乙烯裂解原料优化和炼油化工一体化”的文章以及其他特邀授课专家的论述,以飨读者。编者希望以此能够引起共鸣,对促进我国乙烯工业的发展和乙烯技术的创新有所启迪。     

神经网络遗传算法在氯碱生产过程中的应用

对氯碱产业而言,氯碱电解槽的电解一直是其主要能耗。笔者用BP基于神经网络遗传算法的方法对氯碱电解槽的电解效率进行优化,并在某厂3万t/a烧碱离子膜车间的电解效率进行试验。优化之前,该电解槽电流效率为92.75%;利用神经网络遗传算法优化后,电流效率达到93.36%,带来的经济效益可达200万元/a。     

BP神经网络在乙烯裂解炉预测分析中的应用研究

利用MATLAB工具箱中的BP神经网络模型建立了乙烯裂解炉的三层神经网络模型,应用该模型分析和预测了裂解产物乙烯和丙烯的收率。预测结果与生产过程数据的比较表明,该模型能适合实际生产过程,可用于乙烯生产的预测分析和预测控制。     

国产化裂解技术在小型乙烯改扩建中的应用

在中原乙烯装置及天津乙烯装置的扩能改造中，各新增了1台采用国产化CBL裂解技术的裂解炉，文中介绍了该裂解炉的工艺特点，及其与以前建成的CBL裂解炉相比的技术改进之处，并介绍了对两台裂解炉进行的工艺技术考核结果。     

应用BP神经网络预测加氢裂化尾油蒸汽裂解产物收率

研究了BP神经网络对加氢裂化尾油蒸汽裂解产物收率的预测,并介绍了该BP神经网络的结构、基础数据以及研究结果。从预测数据和实验数据对比结果来看,应用神经网络对加氢裂化尾油裂解产物收率进行预测是可行的。结论是：在保证基础训练数据的准确性和合理选取的情况下,BP神经网络将是一种有效的预测加氢裂化尾油裂解产物收率的方法。     

模糊多目标粒子群算法在乙烯裂解工业中的应用研究

文中研究了模糊多目标粒子群算法（MOPSO）在乙烯裂解工业中应用。算法在Pareto排序基础上引入子目标的最优操作条件来扩展属于非劣解集的操作条件范围,使非劣解集对于每个单目标而言都有较广的覆盖范围,确保非劣解集（操作条件）均匀分布,改进了非劣解集的质量,同时对非劣解引入工况实际要求,通过后验的模糊评价,来确定非劣解的满意操作条件,为决策者提供了明确的操作条件。将模糊多目标粒子群算法用于解决乙烯裂解过程中乙烯和丙烯收率多目标优化问题,较好地平衡了两种目标之闯的冲突,为流程工业多目标优化问题提供了理论指导。     

遗传算法和神经网络的结构优化策略

提出了基于遗传算法和神经网络的结构优化方法。在该方法中,将个体繁殖和群体进化局限于可行域空间。利用该方法求解了２个结构优化实例。     

A GENETIC NEURAL FUZZY SYSTEM AND ITS APPLICATION IN QUALITY PREDICTION IN THE INJECTION PROCESS

A genetic neural fuzzy system (GNFS) is presented and introduced to quality prediction in the injection process. A hybrid-learning algorithm is proposed, which is divided into two stages to train GNFS. During the first learning stage, the genetic algorithm is used to optimize the structure of GNFS and the membership function of each fuzzy term because of its capability of parallel and global search. On the basis of the first optimized training stages, the back-propagation algorithm (BP algorithm) is adopted to update the parameters of the GNFS to improve its predicting precision and reduce the computation time. The process of constructing a quality prediction model for an injection process based on GNFS is described in detail. The predicted weight of the molded part from the model based on GNFS demonstrates that the proposed GNFS has superior performance and good generalization capability in quality prediction in the injection process.     

A GENETIC NEURAL FUZZY SYSTEM AND ITS APPLICATION IN QUALITY PREDICTION IN THE INJECTION PROCESS

A genetic neural fuzzy system (GNFS) is presented and introduced to quality prediction in the injection process. A hybrid-learning algorithm is proposed, which is divided into two stages to train GNFS. During the first learning stage, the genetic algorithm is used to optimize the structure of GNFS and the membership function of each fuzzy term because of its capability of parallel and global search. On the basis of the first optimized training stages, the back-propagation algorithm (BP algorithm) is adopted to update the parameters of the GNFS to improve its predicting precision and reduce the computation time. The process of constructing a quality prediction model for an injection process based on GNFS is described in detail. The predicted weight of the molded part from the model based on GNFS demonstrates that the proposed GNFS has superior performance and good generalization capability in quality prediction in the injection process.     

基于神经网络的遗传算法优化及其应用

遗传算法是 1种模拟自然进化而提出的简单、高效的组合优化算法。研究了磷酸羟胺制备过程中羟胺反应效率的优化问题 ,该过程由于其反应动力学的固有复杂性而无法建模。表明结合神经网络理论 ,遗传算法可以有效地解决这一过程的寻优问题     

ARTIFICIAL NEURAL NETWORKS: PRINCIPLE AND APPLICATION TO MODEL BASED CONTROL OF DRYING SYSTEMS - A REVIEW

Abstract

This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with upto-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN     

ARTIFICIAL NEURAL NETWORKS: PRINCIPLE AND APPLICATION TO MODEL BASED CONTROL OF DRYING SYSTEMS - A REVIEW

This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with upto-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN     

人工神经网络-遗传算法优化激光-等离子体化学气相沉积Si3N4薄膜制备工艺

应用人工神经网络建立了激光－等离子体辅助化学气相沉积（LPCVD）工艺参数与Si3N4薄膜显微硬度的关系。并运用遗传算法优化出了最佳工艺。     

人工神经网络及其在硅酸铝纤维寿命预测中的应用

本文分析了人工神经网络的特点及模型 ,以及其在普通硅酸铝纤维寿命预测中的应用。     

APPLICATION OF GENETIC ALGORITHM FOR OPTIMIZATION OF SEPARATOR PRESSURES IN MULTISTAGE PRODUCTION UNITS

In this article, a genetic algorithm is used to optimize the separator pressures in a multistage crude oil and multistage gas condensate production unit with four and three separators respectively. This leads to the generation of more accurate results for the quality and quantity of oil remaining in the stock tank for both crude oil and gas condensate production units. Genetic-based optimized pressures for crude oil separators resulted in 1.8% and 2% enhancement in oil remaining in the stock tank for summer and winter respectively. API gravity of the stock tank oil was improved 2.4% in summer and 2.2% in winter. For the gas condensate production unit, optimized pressures can enhance by 8.6% and 8.1% the oil remaining in the stock tank for summer and winter respectively. The API gravity of stock tank liquid also increased by 2.6% for both summer and winter.     

单纯形遗传优化算法在陶瓷配方中应用

采用了遗传算法对Nelder-Mead单纯形法进行优化,提出了单纯形遗传优化算法,利用遗传算法的变异操作来增强单纯形法的全局搜索能力,并解决标准遗传算法局部搜索能力较弱的问题。将该算法应用于陶瓷配方设计,与单纯形法运算的结果进行了对比,实验结果验证了该算法的优越性。