A hybrid global optimization method for design of batch chemical processes

A combined strategy of an improved simulated annealing (SA) algorithm and genetic algorithm is presented, with the goal of reducing the computational expenses. The improvements made on the SA algorithm include two parts, i.e., the adaptive regulating for the step vector, and the dynamic testing for the equilibrium of the Metropolis process. The proposed strategy has both the advantage of SA algorithm, the ability to avoid being trapped in a local optimum, and that of genetic algorithm, the ability to use the information about the searched states for the next iteration. A practical application on geometry optimization of pole shoes in large salient pole synchronous generators is effectively implemented using the strategy. The numerical results show that the number of iterations used by executing the combined strategy are only about 75% of those by executing basic SA algorithm.

Integration of Design and Control Using Efficient PSE Approximations

The main viewpoint of land use planning is to resolve the conflict between competing land uses in one place that is carried out based on their economic, social and environmental suitability. Many different approaches have been designed to resolve this conflict but any of them have done. This study aimed to assess accuracy and validity compared four optimization algorithms, including linear programming (LP), simulated annealing (SA), multi-objective land use allocation (MOLA) and multidimensional choice (MDCHOICE) for land use planning in Gorgan Township. At first, land use allocation was done through the LP algorithm. Then, using achieved pixels quantity from LP optimization, the land uses was optimized in term of spatial suitability by other algorithms. The allocation maps generated through these algorithms were compared using statistical methods and landscape metrics. The map provided through MDCHOICE was used as a reference map and that generated through MOLA, SA and LP as a comparative one in a validation process. The similarities and agreements of the two maps were evaluated with various algorithms in IDRISI and MCK software. The results demonstrate that: (1) It follows that the LP algorithm to determine the optimal number of pixels to lead MDCHOICE has helped in the spatial allocation properly. (2) MOLA had a better performance for development in terms of all landscape metrics and SA algorithm was better in forestry land use allocation. Therefore the quality of spatial allocation both of them needs to be improved. (3) Although the LP algorithm is not superior to the algorithms based on the dimensions studied in this research, in terms of achieving the defined goals and the degree of deviation from the number of cells defined for each land use has the least violation. (4) The superiority of MDCHOICE over MOLA, SA and LP and applicability of various kappa statistics for comparing the maps for a more in depth analysis of agreements and disagreements.

Summary The Simulated Annealing (SA) algorithm has been widely used in geophysical data processing. However, the seri al SA algorithm is limited by low computing efficiency. In this paper, the annealing schedule has been improved in the SA algorithm, and a constrained inversion of magnetotelluric (MT) data using the parallel SA algorithm has been applied. The test of synthetic data shows that the improved Very Fast Simulated Annealing (VFSA) method is significantly better than the traditional method. Additionally, the computing efficiency has been improved using the parallel SA inversion algorithm. This method has also been applied in MT data processing for geothermal exploration and good geological results have been achieved.

In industrial practice, conceptual process design is typically conducted by repetitive simulation studies, which require detailed design specifications in an early design phase. Guided by heuristics, these iterative solution procedures result in high manual effort and, in addition, no guarantee concerning the quality of the solution can be given. Optimization-based design methods provide a tremendous potential to accelerate and improve conceptual process design. For this purpose, a synthesis framework for the optimization-based design of chemical processes is presented in this thesis. Powerful shortcut and rigorous evaluation methods for reaction and distillation are presented. These methods are computationally efficient in order to allow an optimization-based design of large-scale chemical processes. Various industrial case studies illustrate the application of the novel approaches and highlight their benefits. ...

For the congestion problems in asynchronous transfer mode (ATM) networks, a hybrid intelligent controller based on simulated annealing (SA) and reinforcement learning (RL) algorithm is proposed. The SA algorithm is a powerful way to solve hard combinatorial optimization problems, which is used to adjust the parameters of the controller. The RL algorithm shows the particular superiority in ATM networks, which is independent of the mathematic model and just needs simple fuzzy information obtained through trial-and-error and interaction with the environment. With the advantages of the two algorithms, the proposed controller forces the queue size in the multiplexer buffer to the desired value by adjusting the source transmission rate of the available bit rate (ABR) service. Simulation results show that the proposed method can promote the performance of the networks and avoid the occurrence of congestion effectively.

This paper presents a method for robust optimal design of chemical processes under parameter uncertainty. The robust optimal design problem is considered as a multiobjective optimization problem in which the expected cost, the economic robustness and the technical robustness are the three objectives. The number of economic robust alternatives could be obtained from the robust design model with its solution algorithms. A decision-making method is proposed to reduce the number of design alternatives and to decide the best-preferred design based on the technical robustness. Two approaches are proposed, which are different from each other in considering the priority of technical robustness in comparison with other objectives. As an application design problem, the wastewater reuse network design of plating process is addressed under uncertain waste input compositions to the cleaning and rinsing system. The application to the wastewater reuse network of plating process illustrates the efficacy of the proposed method for designing robust processes in both economic and technical aspects.

In this study, simulated annealing (SA) and genetic algorithm (GA) soft computing techniques are integrated to search for a set of optimal cutting conditions value that leads to the minimum value of machining performance. Twointegration systems are proposed; integrated SA–GA-type1 and integrated SA–GA-type2. The considered machining performance is surface roughness (R a) in end milling. The results of this study showed that both of the proposed integration systems managed to estimate the optimal cutting conditions, leading to the minimum value ofmachining performance when compared to the result of real experimental data. The proposed integration systems have also reduced the number of iteration in searching for the optimal solution compared to the conventional GA and conventional SA, respectively. In other words, the time for searching the optimal solution can be made faster by using the integrated SA–GA.

In this paper, we address the flowpath design issue of Automated Guided Vehicle Systems (AGVSs). In particular, we concentrate on the design of unidirectional flowpaths (i.e. vehicles are restricted to travel only in one direction along a given segment of the flowpath). We have developed intelligent heuristics — simulated annealing and tabu search algorithms for the design of unidirectional AGVSs. Different versions of simulated annealing and tabu search algorithms are implemented. Our extensive computational results indicate that both simulated annealing and tabu search yield solutions of adequate quality for all practical purposes. A tabu search implementation with the use of a frequency-based memory structure dominates all tested heuristics in terms of solution quality (i.e. percent deviation from optimality), with an impressive average performance over 45 test problems of less than 0.85% deviation from optimality.

Optimization problem of complex system under uncertainty

A new thermodynamically consistent quasi‐Newton formula is proposed for building iterative representations of the approximated part of the Jacobian matrix in the hybrid method of chemical process design. This update exploits the fact that the excess enthalpy and activity coefficient functions are homogeneous functions of degree zero in mole numbers which, in turn, gives rise to a natural null space for the approximated part of the Jacobian matrix that is defined by the component molal flow rates. The new formula builds iterative approximated parts that satisfy this null space constraint in addition to the usual overall secant condition and the desired sparsity constraints.The new quasi‐Newton formula is derived using the variational calculus approach first for the nonsparse case. This solution is then extended to the sparse case.The new updating formula, which is applicable to any chemical process design problem that involves nonideal phase equilibria and/or enthalpy of mixing considerations, is compared to the modified Schubert update in the context of the hybrid method. It is also compared to an implementation of Newton's method in which the approximated part of the Jacobian matrix is calculated by finite differences. Using an adiabatic single stage flash problem for a binary mixture with strongly nonideal liquid phase behavior, it is shown that the new updating formula compares favorably with finite differencing and can result in solutions to problems for which the original hybrid method fails.

This paper addressed the modeling and optimization of loading path in T‐shape hydroforming of tubes using Simulated Annealing (SA) algorithm. Analysis of variance shows that some of pre‐selected parameters in loading paths have not significant effect on the deformed tube. Hence, some of optimized parameters found initially, are replaced with their own fixed optimum values in order to seek for the other parameters in more detail by the Simulated Annealing (SA) algorithm. According to the intensity of effectiveness on the deformation, six more important parameters are chosen and their minimum and maximum limitation values are determined. In this case, sixty four different tests for different loading paths are designed by Design of Experiment (DOE) and full factorial method. By using mathematical modeling all required loading parameters are obtained. Proposed models of formability embedded into Simulated Annealing algorithm and optimum value for loading parameters and optimal load paths are found. The obtained results show that more accurate loading path may be found for T‐shape of tube hydroforming.

A hybrid imperialist competitive-simulated annealing algorithm for a multisource multi-product location-routing-inventory problem

In this paper, simulated annealing (SA) algorithm is employed to solve the flowshop scheduling problem with the objective of minimising the completion time variance (CTV) of jobs. Four variants of the two-phase SA algorithm (SA-I to SA-IV) are proposed to solve flowshop scheduling problem with the objective of minimising the CTV of jobs without considering the right shifting of completion time of jobs on the last machine. The proposed SA algorithms have been tested on 90 benchmark flowshop scheduling problems. The solutions yielded by the proposed SA variants are compared with the best CTV of jobs reported in the literature, and the proposed SA-III is found to perform well in minimising the chosen performance measure (CTV) particularly in medium and large size problems.

A modification of the simulated annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be evaluated only through Monte Carlo simulation is proposed. In this modification, the Metropolis criterion depends on whether the objective function values indicate a statistically significant difference at each iteration. The differences between objective function values are considered to be statistically significant based on confidence intervals associated with these values. Unlike the original SA, the proposed method uses a constant temperature. It is shown that the configuration that has been visited most often in the first k iterations converges almost surely to a global optimizer. Computational results and comparisons with other SA algorithms are presented to demonstrate the performance of the proposed SA algorithm.