A note on multivariate non-normal process capability analysis using “mvtnorm” library in R

In this note we examine using Genzbretz and Miwa algorithms to improve estimation of proposition of non-conformance in multivariate normal distributions. This estimation is required in the procedure outlined in Abbasi and Niaki (Int J Adv Manuf Technol 50(5–8):823–830, 2010) to determine process capability index of multivariate non-normal processes.     

Assessment of the generation of chlorinated byproducts upon Fenton-like oxidation of chlorophenols at different conditions

Homogeneous Fenton-like (H(2)O(2)/Fe(3+)) oxidation proved to be highly efficient in the degradation of monochlorophenols but some important issues need to be considered depending on the operating conditions. When using the stoichiometric amount of H(2)O(2) and a dose of Fe(3+) in the range of 10-20mg/L, complete breakdown of 4-CP up to CO(2) and short-chain acids was achieved. Nevertheless, when substoichiometric amounts of H(2)O(2) or lower concentrations of iron were used, significant differences between the TOC measured and the calculated from the identified species were found. These differences were attributed to condensation byproducts, including chlorinated species, formed by oxidative coupling reactions. PCBs, dioxins and dichlorodiphenyl ethers were identified. A solid residue was also formed consisting mainly in carbon, oxygen and chlorine including also Fe. The occurrence of these highly toxic species must be carefully considered in the application of Fenton oxidation to wastewaters containing chlorophenols. The possibility of reducing costs by lowering the H(2)O(2) dose below the stoichiometric one needs to take this into account.     

Solving constrained optimization problems with hybrid particle swarm optimization

Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder–Mead simplex search method (NM-PSO). This article proposes embedded constraint handling methods, which include the gradient repair method and constraint fitness priority-based ranking method, as a special operator in NM-PSO for dealing with constraints. Experiments using 13 benchmark problems are explained and the NM-PSO results are compared with the best known solutions reported in the literature. Comparison with three different meta-heuristics demonstrates that NM-PSO with the embedded constraint operator is extremely effective and efficient at locating optimal solutions.     

Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions

This article proposes the hybrid Nelder–Mead (NM)–Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM–PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search method. In a suite of 17 multi-optima test functions taken from the literature, the computational results via various experimental studies showed that the hybrid NM–PSO approach is superior to the two original search techniques (i.e. NM and PSO) in terms of solution quality and convergence rate. In addition, the presented algorithm is also compared with eight other published methods, such as hybrid genetic algorithm (GA), continuous GA, simulated annealing (SA), and tabu search (TS) by means of a smaller set of test functions. On the whole, the new algorithm is demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for multimodal functions.     

Polyoxyethylene esters of fatty acids: an alternative synthetic route for high selectivity of monoesters

Esterification reaction of fatty acids with polyoxyethylenes has been studied in the presence of solid acid catalysts and the results are compared with those obtained with a classical homogeneous catalyst, p-toluene sulphonic acid. Solid acid catalysts showed very high selectivities for monoesters even under less favorable reaction conditions of 1:1 molar ratios of oleic acid to PEG. Excess amount of PEG is normally required to ensure high selectivity for monoesters in the presence of homogeneous catalysts. The yields for monoesters given by heterogeneous catalysts such as zeolites, HPA and nafion were comparable to that with p-toluene sulphonic acid after 24 h of reaction. Results suggest that solid acid catalysts are potential catalysts for selective synthesis of monoesters from oleic acids and PEG even under severe reaction conditions. A screening study of different solid acid catalysts showed ZSM-5, zeolite-β, and nafion to be better catalysts than zeolite USY and mordenite, the former giving higher selectivity and yield to monoesters. The results suggest that the most important parameters in optimization catalyst performance for monoesters production is the acid strength and less so on other properties such as shape selectivity and concentration of active sites.     

Real-coded genetic algorithm for stochastic optimization: A tool for recipe qualification of semiconductor manufacturing under noisy environments

This paper considers an interesting topic of recipe qualification during each fabrication step of semiconductor manufacturing. In particular, this type of stochastic optimization scheme within a discrete-event simulation process is discussed and a new optimization method, with the capability to anchor robust recipes for batch processing, is proposed, implemented and evaluated. A particular real-coded genetic algorithm (GA) suitable for resolving continuous optimization problems in noisy environments is developed. Four test functions appearing in the literature are used as a test-bed to assess the proposed genetic algorithm via a variety of experimental studies, showing that the new method can produce much more accurate estimates of the true optimum points than the other two optimization procedures, the Nelder-Mead (NM) simplex search procedure and a well-known variant (RS+S9) of Nelder-Mead suitable for stochastic optimization. As such, the new method could serve as a useful tool for process recipe optimization in noisy semiconductor manufacturing environments. Finally, the chemical mechanical planarization (CMP) process, a turnkey process during semiconductor fabrication, is simulated from batch to batch based on the real-data equipment model and the presented algorithm is employed to seek the optimal recipe profile while processing each batch of wafers sequentially through the CMP tool.     

A hybridized approach to optimal tolerance synthesis of clutch assembly

Specifying proper tolerances for manufactured goods results in greater savings and improved performance, which may ultimately determine whether a product succeeds or fails in the marketplace. In the past, tolerance specification has been more an art than a science, and is largely dependent upon experiences. A more scientific and reliable approach is presented in this paper. A hybrid of Nelder-Mead simplex method and particle swarm optimization (NM-PSO) is introduced for the design of tolerance of the machine elements of an overrunning clutch assembly. The objective is to obtain tolerances of the individual components so that the cost of manufacturing and quality loss is minimized. Experimental results demonstrate that hybrid NM-PSO is extremely effective and efficient in locating best-practice optimal solutions compared to geometric programming (GP), genetic algorithm (GA), and particle swarm optimization (PSO) methods.     

A genetic algorithm and a particle swarm optimizer hybridized with Nelder–Mead simplex search

This paper integrates Nelder–Mead simplex search method (NM) with genetic algorithm (GA) and particle swarm optimization (PSO), respectively, in an attempt to locate the global optimal solutions for the nonlinear continuous variable functions mainly focusing on response surface methodology (RSM). Both the hybrid NM–GA and NM–PSO algorithms incorporate concepts from the NM, GA or PSO, which are readily to implement in practice and the computation of functional derivatives is not necessary. The hybrid methods were first illustrated through four test functions from the RSM literature and were compared with original NM, GA and PSO algorithms. In each test scheme, the effectiveness, efficiency and robustness of these methods were evaluated via associated performance statistics, and the proposed hybrid approaches prove to be very suitable for solving the optimization problems of RSM-type. The hybrid methods were then tested by ten difficult nonlinear continuous functions and were compared with the best known heuristics in the literature. The results show that both hybrid algorithms were able to reach the global optimum in all runs within a comparably computational expense.     

Estimating city-level poverty rate based on e-commerce data with machine learning

Electronic Commerce Research - There are many big data sources in Indonesia, for example, data from social media, financial transactions, transportation, call detail records, and e-commerce. These...