Search results for: Pichet Jiraprasertwong

Authors: Pitsanu Tongkhow, Pichet Jiraprasertwong

Abstract:

This research investigates risk factors for defective products in autoparts factories. Under a Bayesian framework, a generalized linear mixed model (GLMM) in which the dependent variable, the number of defective products, has a Poisson distribution is adopted. Its performance is compared with the Poisson GLM under a Bayesian framework. The factors considered are production process, machines, and workers. The products coded RT50 are observed. The study found that the Poisson GLMM is more appropriate than the Poisson GLM. For the production Process factor, the highest risk of producing defective products is Process 1, for the Machine factor, the highest risk is Machine 5, and for the Worker factor, the highest risk is Worker 6.

Keywords: Defective autoparts products, Bayesian framework, Generalized linear mixed model (GLMM), Risk factors.

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Authors: Achiraya Jiraprasertwong, Erdogan Gulari, Sumaeth Chavadej

Abstract:

Among agricultural residues, sugarcane bagasse is one of the most convincing raw materials for the production of bioethanol due to its availability, and low cost through enzymatic hydrolysis and yeast fermentation. A pretreatment step is needed to enhance the enzymatic step. In this study, sugarcane bagasse (SCB), one of the most abundant agricultural residues in Thailand, was pretreated biologically with various microorganisms of white-rot fungus—Phanerochaete sordid (SK 7), Cellulomonas sp. (TISTR 784), and strain A 002 (Bacillus subtilis isolated from Thai higher termites). All samples with various microbial pretreatments were further hydrolyzed enzymatically by a commercial enzyme obtained from Aspergillus niger. The results showed that the pretreatment with the white-rot fungus gave the highest glucose concentration around two-fold higher when compared with the others.

Keywords: Sugarcane bagasse, Microorganisms, Pretreatment, Enzymatic hydrolysis.

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Authors: Pichet Sriyanyong

Abstract:

This paper presents the application of an enhanced Particle Swarm Optimization (EPSO) combined with Gaussian Mutation (GM) for solving the Dynamic Economic Dispatch (DED) problem considering the operating constraints of generators. The EPSO consists of the standard PSO and a modified heuristic search approaches. Namely, the ability of the traditional PSO is enhanced by applying the modified heuristic search approach to prevent the solutions from violating the constraints. In addition, Gaussian Mutation is aimed at increasing the diversity of global search, whilst it also prevents being trapped in suboptimal points during search. To illustrate its efficiency and effectiveness, the developed EPSO-GM approach is tested on the 3-unit and 10-unit 24-hour systems considering valve-point effect. From the experimental results, it can be concluded that the proposed EPSO-GM provides, the accurate solution, the efficiency, and the feature of robust computation compared with other algorithms under consideration.

Keywords: Particle Swarm Optimization (PSO), GaussianMutation (GM), Dynamic Economic Dispatch (DED).

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