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Solutions Study For Chemical Dynamic Optimization And Their Application

Posted on:2006-04-22Degree:DoctorType:Dissertation
Country:ChinaCandidate:B ZhangFull Text:PDF
GTID:1101360152471727Subject:Chemical Engineering and Technology
Abstract/Summary:PDF Full Text Request
Chemical process strictly is the dynamic process, where the states variables vary with the time and the position. Dynamic process is described by dynamic model that is a group of differential equations. Dynamic optimization is to make a performance index optimal by controlling operational variables in dynamic model. For complex dynamic optimization problems, it is difficult to obtain analytic solution. General method, including steepest descent method, conjugate gradient method and Dynamic Programming, is to search piecewise functions as the approximation with numerical methods on the base of Bellman's principle of optimality or the Hamiltonian function. Novel intelligent algorithms based on bionics become more and more popular for solving dynamic optimization problems in recent years. Dynamic Programming and intelligent algorithms are applicable for the case when the gradients are not available.The single optimization method is not feasible and efficient at all time. For specific problems, several integrated strategies of dynamic optimization solutions are developed in this paper. The main idea of integrated strategies is to make the original problems be a series of sub- problems and choose appropriate method for optimization.For the cases when the gradients are not available and the final states are unconstrained, after analyzing the IDP and continuous stochastic algorithms, two novel algorithms named as iterative genetic algorithm (IGA) and iterative ant-colony algorithm (IACA) were developed, of which the main idea was to iteratively execute ant-colony algorithm (or genetic algorithm) and gradually approximate the optimal control profile. By IGA (or IACA), a continuous dynamic optimization problem is transformed to be a discrete problem. IGA and IACA are more concise than IDP because they don't need discretization of states variables, and control profiles at all time stages are optimized simultaneously. They are moreefficient than continuous stochastic algorithms because of searching optimum among finite candidates. They are successfully applied to optimizing feed-rate of Lee-Ramirez bioreactor; the results show they provide better result at faster speed.For the cases when some final states are fixed, after analyzing the merits of penalty function strategy, a novel strategy named as graded optimization was developed. By graded optimization, a fixed boundary problem is transformed to be series of free boundary problems. It has two schemes. The one is to firstly find feasible control profiles that meet fixed boundary requirements and then choose the best control from feasible profiles to satisfy the real objective. This scheme is also applicable to static optimization with equality constraint. The other one is to firstly compute optimal control profile under given values of adjoint variables and then to adjust those values gradually until fixed boundary requirements were satisfied. Moreover, implementation algorithms were given when the gradients are available. Case studies show graded optimization is feasible. It has the abilities to avoid demerits of penalty function methods and meet fixed boundary requirements with enough accuracy.For the case of free final time, after analyzing the merits of simultaneous optimization, a novel strategy named as two-step optimization was developed. By two-step optimization, a free final time problem is transformed to be series of fixed final time problems that is simpler. Two-step optimization is firstly to compute optimal control profile under a given final time and then to adjust final time accordingly until optimal final time is achieved. Moreover, implementation algorithms were given when the gradients are available. Case studies show two-step optimization is feasible. It has better stability than simultaneous optimization. Integrated with the second scheme of graded optimization, two-step optimization can also be used for the problems with free final time and fixed final states.Neutralization process pH control is a special dynamic optimization problem. According to the math...
Keywords/Search Tags:chemical dynamic optimization, integrated strategy, iterative genetic algorithm, iterative ant-colony algorithm, graded optimization strategy, eugenic strategy, general regression neural network
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