Important parameters on apple drying process are investigated experimentally and modeled employing artificial neural network and neuro-Taguchi's method. Experimental results show that the apple drying curve stands in the falling rate period of drying. Temperature is the most important parameter that has a more pronounced effect on drying rate than the other two parameters i.e. air velocity and the thickness of apple slices. In order to model the drying process, a software was developed which uses the error back propagation algorithm for training. At first, the software was used to simulate the time-dependent variations of moisture content using neural network. Then in order to model the time derivation of moisture ratio in break point, the software was utilized in two ways. First, it was used with no use of any optimization method for modeling the process. In the other approach, the software in a hybrid fashion with Taguchi's method as an optimization method is utilized to correct weight matrix entries. The results demonstrate that the use of neuro-Taguchi's method can give some improvements over neural network accuracy as compared with conventional neural networks approach. By using neuro- Taguchi's method, error is reduced by about 46.4%.

Over the last four decades of research, numerous cell formation algorithms have been developed and tested, still this research remains of interest to this day. Appropriate manufacturing cells formation is the first step in designing a cellular manufacturing system. In cellular manufacturing, consideration to manufacturing flexibility and productionrelated data is vital for cell formation. The consideration to this realistic data makes cell formation problemvery complex and tedious. It leads to the invention and implementation of highly advanced and complex cell formation methods. In this paper an effort has been made to develop a simple and easy to understand/implement manufacturing cell formation heuristic procedure with considerations to the number of production and manufacturing flexibility-related parameters. The heuristic minimizes inter-cellular movement cost/time. Further, the proposed heuristic is modified for the application of principal component analysis and Taguchi’s method. Numerical example is explained to illustrate the approach. A refinement in the results is observed with adoption of principal component analysis and Taguchi’s method.

In this study, feasibility of using seawater to neutralize alkaline red mud for its safe disposal has been studied using Taguchi’s design of experimental methodology. Parameters such as weight of red mud, volume of seawater, stirring time and temperature were tested at three levels to study their effect on response characteristic, i.e., pH of the neutralized slurry. The analysis of variance showed that volume of seawater added and quantity of red mud are the two significant parameters with 53.59 and 44.92% contribution each, respectively. Under the optimized parameters, pH value of red mud slurry reaches to about 8.0 which is within disposable limits. When seawater or other Ca- and Mg-rich brines are added to caustic red mud, the pH of the mixture is reduced causing hydroxide, carbonate or hydroxy carbonate minerals to be precipitated. This mechanism of neutralization process has been explained with emphasis on chemical analysis, mineralogy and morphology of the neutralized red mud. The process improved the physical characteristics of red mud with entrained liquor becoming non-hazardous water with reduced alkalinity. The results would be extremely useful in the process of safe disposal of red mud.

In this paper, Taguchi method was applied to determine the optimum conditions for Zn (II) removal from aqueous solution by halloysite nanotubes (HNTs). An orthogonal array experimental design (L16 (45) which is of five control factors including pH, t (contact time), m (adsorbent mass), T (temperature) and C0 (initial concentration of Zn (II)) having four levels was employed. Adsorption capacity (mg/g) and removal percent (%) were investigated as the quality characteristics to be optimized. In order to determine the optimum levels of the control factors precisely, range analysis and analysis of variance were performed. For removal percent, the optimum condition was found to be pH=6, T=35oC, w=0.4 g, and C0=50 mg/L. Under these optimum conditions, adsorption capacity and removal percent can reach to 132.16 mg/g and 99.76%, respectively.

To analyze how temperature distribution affects particle deposition in an enclosed space, different placements for heating and cooling systems (HC) are examined and the quantity of particles deposited is determined. Taguchi’s optimization method is implemented for finding the best location in order to reduce the number of suspended particles. To evaluate the effect of particle size on the deposition rate for various HC system locations, particles with different sizes (100 nm to 10 µm) are considered. With respect to the results, the location of the HC system does not have a significant effect on the deposition rate of small-sized particles (1 µm and 100 nm), but for particles with a size of 10 µm, where the most of the Covid-19 and Sars viruses are in this range, the HC system’s location significantly affects the number of suspended particles. Therefore, the optimization process with Taguchi’s method has been carried out for the particle with a size of 10 micrometers. Furthermore, the findings indicate that the vertical placement of the heater system is the most important factor and the longitudinal position of the cooler system is the least important factor in the reduction of suspended particles.