Pervaporation membrane reactor study for the esterification of acetic acid and isobutanol using polydimethylsiloxane membrane

In this study, isobutyl acetate, a valuable solvent in cosmetics, aroma and paint industries, is produced by pervaporation–esterification equilibrium reaction in a batch pervaporation membrane reactor (PVMR) using homogeneous (sulphuric acid) and heterogeneous (Dowex 50W-X8) catalysts. The effects of catalyst loading, catalyst type, reaction time, membrane thickness, temperature and initial molar ratio of reactants were investigated. A cross-linked polydimethylsiloxane (PDMS) membrane selective to esters was prepared and used in PVMR. Batch reactions were carried out also in a simple batch reactor (SBR) without pervaporation under the same conditions to compare the conversions for the reactions with and without pervaporation. In conclusion, PVMR experiments showed that the PDMS membrane can be used to remove the isobutyl acetate formed selectively with acceptable conversions and pervaporation fluxes.     

Ultrasound-assisted adsorption of textile dyes using modified nanoclay: Central composite design optimization

The removal of two anionic dyes, C.I. Acid Orange 7 (AO7) and C.I. Acid Red 17 (AR17), by ultrasound-assisted adsorption on the modified nanoclay in aqueous solutions was studied. The modified nanoclay was characterized by SEM/EDX, BET, XRD and FT-IR techniques. The average crystal size for the interlayer spacing of the modified nanoclay was about 14.3 nm. Central composite design (CCD) was used for the optimization of the operational parameters, including the initial dye concentration, sonication time, adsorbent dosage and temperature. The results demonstrated a good agreement between the predicted values obtained by the model and the experimental values for both AO7 (R²= 0.959) and AR17 (R²=0.971).     

A review of applications of genetic algorithms in lot sizing

Lot sizing problems are production planning problems with the objective of determining the periods where production should take place and the quantities to be produced in order to satisfy demand while minimizing production, setup and inventory costs. Most lot sizing problems are combinatorial and hard to solve. In recent years, to deal with the complexity and find optimal or near-optimal results in reasonable computational time, a growing number of researchers have employed meta-heuristic approaches to lot sizing problems. One of the most popular meta-heuristics is genetic algorithms which have been applied to different optimization problems with good results. The focus of this paper is on the recent published literature employing genetic algorithms to solve lot sizing problems. The aim of the review is twofold. First it provides an overview of recent advances in the field in order to highlight the many ways GAs can be applied to various lot sizing models. Second, it presents ideas for future research by identifying gaps in the current literature. In reviewing the relevant literature the focus has been on the main features of the lot sizing problems and the specifications of genetic algorithms suggested in solving these problems.     

The state of the art on buffer allocation problem: a comprehensive survey

The buffer allocation problem is an NP-hard combinatorial optimization problem and it is an important research issue in designing manufacturing systems. The problem deals with finding optimal buffer sizes to be allocated into buffer areas in a production system to achieve a specific objective. This paper presents a comprehensive survey on buffer allocation problem in production systems. To provide a systematic review of current relevant research, first studies are grouped in two categories: 1. Reliable production lines, 2. Unreliable production lines. Next, the studies in each group are reviewed based on topology of the production line, the solution methodologies suggested and the objective function employed. The aim of this review is twofold. First, it provides an overview of recent advances in the field in order to highlight the new trends in solution methodology. Second, it presents ideas for future research by identifying gaps in the current literature.     

Joint optimization of spare parts inventory and maintenance policies using genetic algorithms

In general, the maintenance and spare parts inventory policies are treated either separately or sequentially in industry. However, since the stock level of spare parts is often dependent on the maintenance policies, it is a better practice to deal with these problems simultaneously. In this study, a simulation optimization approach using genetic algorithms (GAs) has been proposed for the joint optimization of preventive maintenance (PM) and spare provisioning policies of a manufacturing system operating in the automotive sector. A factorial experiment was carried out to identify the best values for the GA parameters, including the probabilities of crossover and mutation, the population size, and the number of generations. The computational experiments showed that the parameter settings given by the proposed approach achieves a significant cost reduction while increasing the throughput of the manufacturing system.