https://matjournals.net/engineering/index.php/JoIM/issue/feed 2026-08-29T08:51:39+00:00 Open Journal Systems <p><strong>JoIM</strong> is a peer reviewed Journal in the discipline of Engineering published by the MAT Journals Pvt. Ltd. The Journal provides a platform to Researchers, Academicians, Scholars, Professionals and students in the Domain of Mechanical Engineering to promulgate their Research/Review/Case studies in the field of Industrial Mechanics Engineering. The Journal aims to promote high quality empirical Research, Review articles, case studies and short communications mainly focused on Safety Engineering, Management Science, Operations Research, System Engineering, Management Engineers, Industrial Plant, Engineering Design Process, Textile Industry, Materials Management, Human Resource Management.</p> https://matjournals.net/engineering/index.php/JoIM/article/view/3900 2026-07-23T09:42:44+00:00 Ritesh G Upadhyay urmila.chauhan@vidhyadeepuni.ac.in Urmila Vivek Chauhan urmila.chauhan@vidhyadeepuni.ac.in

<p><em>Separation processes are energy intensive and a major contributor to operating costs in the chemical industry. This drives the need for more efficient and sustainable alternatives to conventional unit operations. Hybrid separation processes, which combine two or more separation techniques into a single process framework, have been developed as an effective strategy to overcome the limitations of individual separation methods. Hybrid systems, which combine complementary technologies such as membranes, adsorption, absorption, extraction, distillation, and reactive separations, can lead to improved selectivity, enhanced product purity, and significant reductions in energy demand and equipment size. This article describes the principles and industrial uses of hybrid separation processes in chemical engineering with a focus on their contribution to process intensification and sustainable manufacturing. The major hybrid configurations such as membrane-distillation, membrane-absorption, adsorption-distillation and extraction-distillation systems are reviewed in terms of their performance merits and operational challenges. Hybrid separation strategies are illustrated by industrial case studies ranging from petrochemical refining to pharmaceutical manufacturing, bioprocessing, food and beverage industries, and environmental applications such as wastewater treatment and carbon capture to demonstrate their practical benefits. The integration of advanced materials, process modelling, and digital optimization tools has further enhanced the feasibility and industrial adoption of hybrid separation technologies. Despite challenges related to process integration, scale-up, and economic evaluation, hybrid separation processes offer a viable pathway toward energy-efficient, low-carbon, and flexible chemical manufacturing. This review highlights current trends, challenges, and prospects, emphasizing the critical role of hybrid separation systems in the next generation of industrial chemical processes.</em></p>

2026-07-23T00:00:00+00:00 Copyright (c) 2026 Journal of Industrial Mechanics https://matjournals.net/engineering/index.php/JoIM/article/view/4054 2026-08-29T08:51:39+00:00 Briggs Otekenari Tonye tonye.briggs@ust.edu.ng Jack Sotonte Emmanuel tonye.briggs@ust.edu.ng

<p><em>Vibration-based Predictive Maintenance (PdM) will prove to be one of the most significant developments in industrial asset management in the last 20 years. This systematic review provides an overview of mechanical vibration analysis evolution from simple periodic inspection and calendar-based scheduling to the current deep learning-supported edge deployment of diagnostic architectures with Industry 4.0 goals. The review summarizes the signal processing techniques, such as the Short-Time Fourier Transform (STFT), Empirical Mode Decomposition (EMD), wavelet packet transforms, cyclostationary analysis, and their combinations, and illustrates their linkage with machine learning classifiers and deep learning networks, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, transformer networks, Graph Neural Networks (GNNs), and Physics-Informed Neural Networks (PINNs). Significant implementation issues in the central challenges are discussed in detail: data scarcity, class imbalance, non-stationarity, edge resource constraints, model interpretability, and cybersecurity concerns in IIoT deployments. While several emerging mitigation strategies such as transfer learning, generative adversarial augmentation, TinyML quantisation, transparent operator networks, and federated learning are explored, there are still gaps that can be evaluated. Lastly, there are six priority research directions are identified: zero-shot diagnostics, digital twin enhanced training, foundation model integration, adversarially resilient pipelines, federated multi-plant learning, and sustainable sensor lifecycle management. The review shows how making full use of autonomous prescriptive maintenance requires computational efficiency, physical interpretability, and uniform ethical oversight.</em></p>

2026-08-29T00:00:00+00:00 Copyright (c) 2026 Journal of Industrial Mechanics