陶瓷广告的语言特征及翻译策略

陶瓷广告语具有独特的语言风格和特点.其用词优美独到,句法精练而内涵丰富.修辞变化多端,耐人寻味.陶瓷广告的目的在于打动人心,激发人们的购买欲望.一字一句要能充分发挥它的"魅力",并达到其商业目的.本文从陶瓷广告语的风格和特点着手,探讨了在翻译陶瓷广告语时,应根据陶瓷广告的不同特点,采用直译法、意译法,套译法等实用方法.     

聚丙烯的出路及后加工发展策略

１液相本体法聚丙烯的发展概况１．１国外发展概况液相本体法聚合生产工艺是聚丙烯工业化的中后期发展起来的新工艺。美国Ｄａｒｔ公司Ｒｅｘａｌｌ分公司在聚丙烯实现工业化７年后的１９６４年,首先采用ＴｉＣｌ３-ＡｌＣｌ３（第１代催化剂）和釜式反应器,开创了本体法聚丙烯工艺的先河。２０世纪７０年代日本宇部兴产、三井东压、三井油化、昭和电工、德山曹达、美国Ｅｌｐａｓｏ及Ｐｈｉｌｌｉｐｓ等公司也都实现了液相本体法聚丙烯的工业化。实际上,液相本体法聚丙烯的工业化的兴起与催化剂的发展有着直接的关系。２０世纪６０年代末,…     

采用结构进化策略的Lagrange乘子法优化换热网络

针对罚函数法处理有约束问题时存在的不足,采用Lagrange乘子法优化换热网络.为求解Lagrange函数方程组,根据确定性方法,提出最速下降法求解策略以及Powell法求解策略.通过极小值判断机制,保证Lagrange函数方程组的解是原换热网络目标函数值的极小值.根据实际工况,提出结构进化策略,与Lagrange乘子法相结合,实现了换热网络全局最优化.通过经典算例验证了两种求解策略的有效性、准确性以及结构进化策略的通用性.与文献结果进行对比,结果表明本算法具有较强的局部搜索能力以及全局搜索能力,能够找到更优的换热网络结构,有利于在工业生产中节约成本.     

反渗透海水淡化系统中膜组件的清洗策略

反渗透海水淡化法中膜组件的定期清洗是防治膜污染的主要措施之一.膜清洗可将膜表面的污染物去除,达到恢复透水量的目的.在保证产水量的情况下,选择恰当的清洗策略和清洗时机可使清洗次数最合理,清洗费用最低.以规划期内总操作费用最小为目标,提出了新的污染模型,给出了清洗的判据,将清洗策略问题表达为一个混合整数非线性规划（MINLP）.对一个实例进行计算,给出了该例中的反渗透系统在5年规划期内的最优清洗策略和清洗时机.结果证明这种数学规划方法对于寿命为3～5年的膜组件的清洗维护是有效的.     

我国磷化工产业发展面临的形势及发展策略

针对我国磷化工产业发展现状及存在的问题,特别是面临国际硫磷资源价格攀升的严峻形势,提出目前我国磷化工发展的相应策略是:发展精细磷化工;走热法磷酸的技术路线;注意发展磷化工产业的循环经济,节能减排,提高竞争力;开发磷石膏的综合利用;开发利用低品位磷矿、不用硫酸生产磷酸的方法(窑法磷酸)。     

元认知策略在大学英语听力课上的作用

元认知策略是学习者为了成功学习一门外语而采取的管理步骤(也称调控策略).在听力教学中进行元认知策略培训是提高听力理解能力的有效途径.本文主要探讨了元认知策略在大学英语听力课中的作用.     

基于CSTR的反应器网络综合目标策略法

根据构造目标函数法和超结构法的思想,采用基于CSTR的反应器网络模型,对Van-de-Vusse反应体系的流程结构进行了研究。通过建立数学模型并且优化可以看出,相对于超结构法以及三参数分布法,该方法所获得的流程结构更简洁有效。     

异轮烷合成方法的研究进展

综述了几种成功合成异轮烷的策略,如正交结合法、自分类法、协同捕获法以及金属模板活化法等。指出基于异轮烷的结构及其独特性能,异轮烷被进一步用于构筑分子梭、超分子加密系统、光异构催化剂等。最后对异轮烷未来的研究方向进行了展望。     

技术标准与自主技术创新的探讨

本文阐述了我国石油工业技术和技术标准与发达国家的差距,探讨了石油工业发展的关键是自主技术创新,发展的目标是创新技术标准,以及中国石油工业技术标准的发展策略.     

高级氧化技术在水处理的研究进展

高级氧化技术(AOPs)是目前很受人们关注的新型水处理工艺,特别是对难降解有机废水有其独到之处.着重介绍了Fenton法及类Fenton法、光化学氧化法和光催化氧化法、臭氧氧化法、电化学氧化法、湿式氧化法和湿式催化氧化法、超临界水氧化法及超临界水催化氧化法在水处理中的作用机理和研究进展.如何产生更多的羟基自由基(·OH)是AOPs面临的一个主要问题.研究价格低廉、氧化效率好的氧化剂和催化性能好、稳定性强的催化剂是今后AOPs的主要发展趋势.     

进化规划-蚁群优化算法的构建并用于化工过程操作优化

经典蚁群优化（ACO）算法搜优效率高,但只适用于求解组合优化等离散问题.以搜索最优食物源为目标,并引入进化规划（EP）简洁的进化机制,用以改造ACO,使之适于连续问题.又将蚁群分工为全局和局部蚂蚁,分别引领个体进行全局探索式和局部挖掘式寻优,并在各个体上释放信息素,供蚁群共享,由此继承了ACO正反馈、互激励的优点,并在优进策略的支持下,构建为EP-ACO算法.经复杂测试函数的优化检验,显示出EP-ACO适于连续问题,且全局搜优效率高,对高维问题适应性强.将EP-ACO应用于二甲苯异构化装置的操作优化,取得了良好的效果,与其他方法相比,优越性明显.     

Optimization-based design of reactive distillation columns using a memetic algorithm

The design optimization of reactive distillation columns (RDC) is characterized by complex nonlinear constraints, nonlinear cost functions, and the presence of many local optima. The standard approach is to use MINLP solvers that work on a superstructure formulation where structural decisions are represented by discrete variables and lead to an exponential increase in the computational effort. The mathematical programming (MP) methods which solve the continuous sub-problems provide only one local optimum which depends strongly on the initialization. In this contribution a memetic algorithm (MA) is introduced and applied to the global optimization of four different formulations of a computational demanding real-world design problem. An evolution strategy addresses the global optimization of the design decisions, while continuous sub-problems are efficiently solved by a robust MP solver. The MA is compared to MINLP techniques. It is the only algorithm that finds the global solution in reasonable times for all model formulations.     

离散粒子群算法的波纹管优化设计研究

为了解决波纹管所涉及的非线性约束离散变量的优化设计问题,文中尝试将粒子群算法与惩罚函数法相结合,建立新型离散粒子群算法,实现离散变量与连续变量之间的转化。提出了一种新的离散惩罚因子更新策略,以保证离散解的精度及算法的收敛性。通过著名的容器设计算例验证,文中方法优于文献方法。用该方法对波纹管工程实例进行优化设计,优化目标值比在用产品提高了79.96%,且与理论值接近,离散解的精度满足要求,进一步证明了该方法在求解波纹管工程非线性约束离散变量优化设计问题时的有效性。     

One-step approach for heat exchanger network retrofitting using integrated differential evolution

Heat exchanger network (HEN) retrofitting is more important and challenging than HEN synthesis since it involves modifying existing network for improved energy efficiency. Additional factors to be considered include spatial constraints, relocation and re-piping costs, reassignment and effective use of existing heat exchanger areas. The previous studies using stochastic global optimization algorithms are mainly focused on two-level approach: the first level uses a stochastic algorithm for optimizing structure, and the second level uses either a stochastic or a deterministic algorithm for optimizing continuous variables. In this study, we propose and test one-step approach where a stochastic global optimization method, namely, integrated differential evolution (IDE), handles both discrete and continuous variables together. Thus, HEN structure and retrofitting model parameters are simultaneously optimized by IDE, which avoids the algorithm trapping at a local optimum and also improves the computational efficiency. Results on HEN applications show that the proposed approach gives better solutions.     

基于微粒群优化算法的不确定性调和调度

Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimization problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization （PSO） algorithm is developed for nonlinear optimization problems with both continuous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.     

迭代遗传算法及其用于生物反应器补料优化

针对化工动态优化的数值求解问题,提出将迭代思想与遗传操作相结合,构建迭代遗传算法.算法首先对时间区间和控制搜索域实施离散化,进而应用遗传操作搜索离散问题的最优控制策略.逐步收缩搜索域并迭代以消减离散化带来的偏差,不断改善寻优结果,增强算法的稳健性.实例测试表明该算法简便、可行、高效,已成功地应用于Lee-Ramirez生物反应器补料流率的优化,运算结果优于文献值,显示了迭代遗传算法的优越性.迭代遗传算法尤其适用于系统的梯度信息不可得的情况.     

Optimum design of heat exchanger networks

The optimum design of heat exchanger networks is considered in two stages. In the first, the optimum configuration for fixed values of the continuous variables is determined using an implicit enumeration algorithm. In the second stage the optimization of continuous variables of the network is performed solving a large scale nonlinear programme. The proposed method for discrete variables lessens considerably the computational effort for solving the combinatorial problem in the first stage. The results show the importance of optimizing the continuous variables of these networks.     

A Mixed Coding Scheme of a Particle Swarm Optimization and a Hybrid Genetic Algorithm with Sequential Quadratic Programming for Mixed Integer Nonlinear Programming in Common Chemical Engineering Practice

In this paper, mixed integer nonlinear programming (MINLP) is optimized by PSO_GA–SQP, the mixed coding of a particle swarm optimization (PSO), and a hybrid genetic algorithm and sequential quadratic programming (GA–SQP). The population is separated into two groups: discrete and continuous variables. The discrete variables are optimized by the adapted PSO, while the continuous variables are optimized by the GA–SQP using the discrete variable information from the adapted PSO. Therefore, the population can be set to a smaller size than usual to obtain a global solution. The proposed PSO_GA–SQP algorithm is verified using various MINLP problems including the designing of retrofit heat exchanger networks. The fitness values of the tested problems are able to reach the global optimum.     

一种改进的混合遗传算法及在化工过程动态优化中的应用(英文)

The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. Genetic algorithm (GA) has been proved to be a feasible method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm (HIGA) for solving dynamic optimization problems is proposed to overcome these defects. Simplex method (SM) is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Gaussian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.     

一种应用于换热网络综合的阻尼优化方法

针对启发式方法优化换热网络在优化后期易陷入局部极值的问题,提出一种阻尼优化方法,即通过引入延缓概率的概念,以一定的概率不接受费用下降的结构,延缓该结构形成固定匹配,避免因连续变量优化过快导致整型变量优化不充分而陷入局部最优。通过探讨不同阶段的优化特点及优化陷入局部极值的成因,进而提出分阶段延缓策略,合理调控延缓条件以及延缓概率的取值,从而提高算法的全局搜索能力。最后采用四个不同规模的算例进行验证,结果表明该方法可有效地跳出局部最优解,促进结构的进一步优化。