The selection of manufacturing processes for a given application is a complex problem of multicriteria decision‐making although there have been several different approaches that can be utilized to select a suitable alternative. However, identifying appropriate multicriteria decision‐making approach from the list of available methods for a given application is a difficult task. This work suggests a methodology to assess different selection approaches, which are the technique for order of preference by similarity to ideal solution (TOPSIS), analytic hierarchy process (AHP), and VIKOR: stepwise procedure. This valuation was done depending on the following factors: number of alternative processes and criteria, agility through the process of decision‐making, computational complexity, adequacy in supporting a group decision, and addition or removal of a criterion. A case study in this study was presented to analyse the evaluation methodology. The criteria used to evaluate and identify the best manufacturing process were categorized into productivity, accuracy, complexity, flexibility, material utilization, quality, and operation cost. Five manufacturing processes were considered, including gravity die casting, investment casting, pressure die casting, sand casting, and additive manufacturing. The results showed that each approach was suitable for the problems of manufacturing process selection, in particular toward the support of group decision‐making and uncertainty modelling. Manufacturing processes were ranked based on their respective weights for AHP, TOPSIS, and VIKOR, and sand casting is the best. In terms of computational complexity, the VIKOR method performed better than TOPSIS and AHP. Moreover, the VIKOR and TOPSIS methods were better convenient to the selection of manufacturing processes for agility during the process of decision‐making, the number of alternative processes and criteria, adequacy in supporting a group decision, and addition or removal of a criterion.

The acid-catalyzed pre-treatment esterification process is required for low-cost feedstock with high free fatty acids (FFAs) to avoid the saponification that occurs during alkali-catalyzed transesterification for the production of fatty acid alkyl esters (FAAE). Reverse hydrolysis in acid-catalyzed esterification causes a decrease in fatty acid methyl ester (FAME) yield. Therefore, the esterification process must be intensified. This study aims to develop and optimize a low-temperature intensification process to enhance biodiesel yield and reduce energy consumption. Three intensification systems were studied: co-solvent technique, co-solvent coupled with adsorption of water using molecular sieves, and entrainer-based continuous removal of water. The process variables of esterification reaction in co-solvents without the adsorption system were optimized by using central composite design (CCD). The study showed that the co-solvent without the adsorption system was effective in intensifying the FFA conversion (X_FFA) at low temperatures, compared to the other two systems, due to the dilution effect at high co-solvent/entrainer amount required for sufficient vapors in the adsorption system. Optimized process variables have achieved 95% X_FFA within 75 min at 55 °C, 20 mL/100 g of oil DEE, 9 MR, 3 wt % H₂SO₄, and 320-350 RPM in a co-solvent without the adsorption system.

Purpose: This study provides a unique integrated diagnosis system to investigate the causes of low productivity, profitability, machinery health conditions and wear severity of medium-size biscuit industry assets in Taiz, Yemen.Design/methodology/approach: The evaluation is based on an integrating of the overall equipment effectiveness (OEE) and oil-based maintenance (OBM) approaches. The data are collected using the company's operational records, interviews and observations, while the used lubricating oil samples are also collected from production lines' machineries. Scanning electron microscope (SEM) is used to study the wear debris particle features and wear mechanism. Different other analysis tools such as fishbone, 5 whys and Pareto charts are also used to investigate the root causes and plausible recovery solutions of machinery failures.Findings: This study demonstrated that a large proportion of machinery failures and production loss are of management concerns. Also, this study inferred that the analysis of wear debris is unique and informative for determining machinery wear severity and useful life. Finally, the current conditions of production lines are clarified and suggestions to use a mixed preventive/predictive maintenance management approach are also elucidated.Originality/value: This work implemented an integrated OEE/OBM diagnostic maintenance system to investigate the root causes of low productivity and machine failures in real production lines and suggested robust decisions on the maintenance duties.

In a world increasingly aware of its carbon footprint, the quest for sustainable energy production and consumption has never been more urgent. A key player in this monumental endeavor is fuel conservation, which helps curb greenhouse gas emissions and preserve our planet’s finite resources. In the realm of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) technologies, Caterpillar (CAT) generator set (genset) operations have been revolutionized, unlocking unprecedented fuel savings and reducing environmental harm. Envision a system that not only enhances fuel efficiency but also anticipates maintenance needs with state-of-the-art technology. This standalone IIoT platform crafted with Visual Basic.Net (VB.Net) and the KEPware Object linking and embedding for Process Control (OPC) server gathers, stores, and analyzes data from CAT gensets, painting a comprehensive picture of their inner workings. By leveraging the Modbus Remote Terminal Unit (RTU) protocol, the platform acquires vital parameters such as engine load, temperature, pressure, revolutions per minute (RPM), and fuel consumption measurements, from a radar transmitter. However, the magic does not stop there. Machine Learning.Net (ML.Net) empowers the platform with machine learning capabilities, scrutinizing the generator’s performance over time, identifying patterns and forecasting future behavior. Equipped with these insights, the platform fine tunes its operations, elevates fuel efficiency, and conducts predictive maintenance, minimizing downtime and amplifying overall efficiency. The evidence is compelling: IIoT and AI technologies have the power to yield substantial fuel savings and enhance performance through predictive maintenance. This research offers a tangible solution for industries eager to optimize operations and elevate efficiency by embracing IIoT and AI technologies in CAT genset operations. The future is greener and smarter, and it starts now.

Most daily tasks require exerting static grip strength which can be challenging for the elderly as their strength diminishes with age. Moreover, normative static grip strength data are important in ergonomics and clinical settings. The goal of this study is to present the gender, age-specific, hand-specific, and body-mass-index-specific handgrip strength reference of Saudi males and females in order to describe the population’s occupational demand and to compare them with the international standards. The secondary objective is to investigate the effects of gender, age group, hand area, and body mass index on the grip strength. A sample of 297 (146 male and 151 female) volunteers aged between 18 and 70 with different occupations participated in the study. Grip strength data were collected using a Jamar dynamometer with standard test position, protocol, and instructions. The mean maximum voluntary grip strength values for males were 38.71 kg and 22.01 kg, respectively. There was a curvilinear relationship of grip strength to age; significant differences between genders, hand area, and some age groups; and a correlation to hand dimensions depending on the gender.

This experimental study explores the integration of Phase Change Materials (PCMs) within building envelopes. The research specifically centers on the utilization of two microencapsulated paraffin-based PCMs with melting points of 37 °C and 43 °C. The study assesses their performance within cement and gypsum-based PCM composites, concentrating on service areas often overlooked in thermal analysis, including underground garages, staircases, and utility rooms. The experimental setup included constructing three chambers inside an underground garage during the hot months of June and July in Saudi Arabia. Two chambers were assigned to integrate the PCM, while the third chamber served as a control without PCM. The experiment unfolds in two phases. In the initial phase, the objective was to determine which PCM is more effective in reducing the heat load inside the chambers. This led to the adoption of the 43 °C PCM for the subsequent stage. The adoption of the 43 °C PCM resulted in a fourfold decrease in heat compared to the 37 °C PCM. The second phase investigates the integration of the selected PCM with cement and gypsum composites. The percentage of PCM incorporated into the concrete and gypsum composites was determined experimentally. For cement-based composites, the identified percentage that maintains material integrity is 20%, and for gypsum-based composites, it is 22%. The findings demonstrate a significant reduction in cooling load with PCM incorporation, with cement-based composites exhibiting superior thermal performance compared to gypsum-based alternatives and reducing the heat load by approximately 63%. Additionally, it was observed that concrete reduced the highest temperature during the day by 5.2 °C, which equates to about a 10% reduction, further enhancing comfort. Conducted over the course of two summer seasons, this study contributes valuable insights toward improving the quality of life for building occupants, considering various factors such as their living environment.