Modeling and Simulation of Manufacturing Processes and Systems: Overview of Tools, Challenges, and Future Opportunities

Manufacturing is an important part of the modern economy. It is characterized by complexity in terms of systems, approaches, and interactions with intrinsic and extrinsic factors. Numerous efforts have been developed to use modeling and simulation tools to improve manufacturing efficiency and productivity and to achieve maximum quality, especially with the different mutations in the factories of today. This paper reviews the conventional and modern tools used in manufacturing system design and production improvement. Challenges that need to be addressed by the simulation community are discussed in depth. Finally, the evolution, advances, current practices, and future opportunities are discussed in the context of the contemporary manufacturing industry

A Comparative Analysis of Hidden Markov Model, Hybrid Support Vector Machines, and Hybrid Artificial Neural Fuzzy Inference System in Reservoir Inflow Forecasting (Case Study: The King Fahd Dam, Saudi Arabia)

The precise prediction of the streamflow of reservoirs is of considerable importance for many activities relating to water resource management, such as reservoir operation and flood and drought control and protection. This study aimed to develop and evaluate the applicability of a hidden Markov model (HMM) and two hybrid models, i.e., the support vector machine-genetic algorithm (SVM-GA) and artificial neural fuzzy inference system-genetic algorithm (ANFIS-GA), for reservoir inflow forecasting at the King Fahd dam, Saudi Arabia. The results obtained by the HMM model were compared with those for the two hybrid models ANFIS-GA and SVM-GA, and with those for individual SVM and ANFIS models based on performance evaluation indicators and visual inspection. The results of the comparison revealed that the ANFIS-GA model and ANFIS model provided superior results for forecasting monthly inflow with satisfactory accuracy in both training (R2 = 0.924, 0.857) and testing (R2 = 0.842, 0.810) models. The performance evaluation results for the developed models showed that the GA-induced improvement in the ANFIS and SVR forecasts was matched by an approximately 25% decrease in RMSE and around a 13% increase in Nash–Sutcliffe efficiency. The promising accuracy of the proposed models demonstrates their potential for applications in monthly inflow forecasting in the present semiarid region

Experimental Investigation of a Pilot Solar-Assisted Permeate Gap Membrane Distillation

This research deals with the process of water desalination, involving an experimental design used to study a new prototype of a solar membrane distillation plant based on the weather conditions of Kairouan City, Tunisia. In this experiment, the pilot is left autonomous with the sun as the only source of energy. The operating process of a desalination plant consists of solar energy provided by the sun using solar energy collectors, which provide energy through their photovoltaic panels for heating brackish water. Additionally, the membrane used in this study was of the spiral wound design, which allowed for a compact arrangement besides effective internal heat recovery. The system start-up was successfully carried out and experimental studies were launched on various days of August 2020. During the experiment, the average production was approximately 15.92 L/m2 ap per day while the distillate’s electoral conductivity amounted to 1865 μS/cm. Calculations revealed that the specific thermal energy consumption for the system ranged between 90 and 310 kWh/m3

Modeling and Simulation of Fabricated Graphene Nanoplates/Polystyrene Nanofibrous Membrane for DCMD

Membrane distillation is an active technique that provides pure water with very good rejection and could be applied to water of extremely high salinity. The low productivity of membrane distillation needs intensive efforts to be competitive with other desalination techniques. In this current study, a composite (PS/GNP) membrane, which is composed of polystyrene (PS) based and 0.25% weight percent graphene nanoplates (GNP) has been fabricated via electrospinning and compared with the blank PS membrane. SEM, FTIR, contact angle and porosity characterization have been performed, and the results show that the validity of the predefined conditions, and the contact angle of the composite membrane, which is found to be 91.68°, proved the hydrophobic nature of the composite membrane. A numerical simulation using Ansys 2020 software has been introduced to study the performance of the fabricated composite membrane when used in direct contact membrane distillation (DCMD). The numerical model has been validated with experimental work from the literature and showed an excellent match. The blank PS and composite PS/GNP membranes have been investigated and compared at different operating conditions, i.e., hot water supply temperature and system flow rate. The results show that the composite PS/GNP membrane outperforms the blank PS membrane at all studied operating conditions