Journal of Recent Activities in Production (e-ISSN: 2581-9771)

Production and Performance Evaluation of Biomass Pellets from Mixed Agricultural Residues with Waste Oil Additive

Sandeep Supalkar, Digambar Arsule, Sachin Sangale, Uddhav Nimbalkar — Fri, 15 May 2026 00:00:00 +0000

The growing demand for sustainable energy and the environmental impacts associated with open-field burning of agricultural residues have accelerated interest in biomass densification technologies. This study investigates the production and performance enhancement of biomass pellets manufactured from a blend of rice husk, wheat straw, and sugarcane bagasse with controlled incorporation of waste engine oil (WEO) as an energy-enriching additive. Pellets were produced using a flat-die pellet mill while maintaining constant processing parameters to isolate the influence of additive concentration. Four formulations containing 0%, 1%, 3%, and 5% WEO were evaluated for bulk density, mechanical durability, moisture, ash content, and higher heating value. Results revealed that moderate oil addition improved particle rearrangement and compaction, increasing bulk density from 612 to 668 kg/m³ at 3% WEO. The calorific value showed a continuous rise from 16.8 to 20.2 MJ/kg, representing nearly a 20% enhancement compared with the reference blend. Durability slightly decreased at higher additive levels due to lubrication-induced reduction in inter-particle friction, yet remained within acceptable commercial limits. Ash content declined marginally with increasing oil proportion, supporting cleaner combustion behavior. Considering the trade-off between energy gain and mechanical strength, the formulation with approximately 3% WEO was identified as the optimum mixture. The study demonstrates a practical pathway for simultaneous valorization of agricultural waste and used lubricating oil, contributing to circular economy practices and decentralized renewable energy solutions.

Development of a Predictive Maintenance Framework Using Machine Learning for CNC Machining Operations in Nigerian Automotive Component Manufacturing Plants

Ogagavwodia Ejovi Okuma, Briggs Otekenari Tonye — Wed, 10 Jun 2026 00:00:00 +0000

Computer Numerical Control (CNC) machining operations in sub-Saharan African automotive manufacturing facilities remain disproportionately reliant on reactive and time-based maintenance strategies, leading to unplanned downtime rates that exceed global benchmarks by a margin of 30–45%. In Nigeria's emerging automotive sector, anchored by Innoson Vehicle Manufacturing Company (IVM) in Nnewi, Anambra State, production losses attributable to CNC tool failure and unscheduled machine stoppages have been estimated at ₦2.3 billion annually, yet systematic predictive maintenance (PdM) frameworks calibrated for the operational realities of this environment remain absent from published literature. This study developed and validated a hybrid machine learning framework integrating Random Forest (RF) classification and Long Short-Term Memory (LSTM) neural network regression to predict cutting tool wear state and imminent machine failure events from multivariate IoT sensor data streams collected directly from CNC turning centres at IVM's Nnewi facility. A sensor array comprising vibration accelerometers, acoustic emission transducers, spindle current monitors, and thermocouple assemblies logged 14 feature variables at 1 kHz sampling frequency across 1,840 operational hours. The RF classifier achieved a tool-wear-state classification accuracy of 93.7% (F1-score: 0.924) on the held-out test set, outperforming a baseline Support Vector Machine (SVM) by 8.4 percentage points. The LSTM regression model predicted the remaining useful life (RUL) of cutting inserts with a Root Mean Square Error (RMSE) of 4.31 minutes and a Mean Absolute Percentage Error (MAPE) of 6.8%, enabling a proactive replacement window of 18–22 minutes before catastrophic failure. Simulated deployment of the framework projected a 34.2% reduction in unplanned downtime and an annual cost saving of approximately ₦786 million against baseline operations. These results demonstrate that context-specific, data-driven maintenance systems are technically feasible and economically compelling for African automotive manufacturing, and provide a replicable methodology for similar low-to-middle-income industrial environments.

Entropy Production and Energy Dissipation in Climate Change Modeling: A Second-Law Perspective

Md. Ali — Mon, 18 May 2026 00:00:00 +0000

This study explores the climate system as a non-equilibrium thermodynamic system driven by solar radiation and balanced by infrared emission to space. Within this framework, entropy production is used as a quantitative measure of irreversibility and energy dissipation across atmospheric, oceanic, and terrestrial processes. A second-law perspective is applied to climate change modeling to assess how entropy production constrains energy conversion, circulation intensity, and feedback behavior. The study separates radiative entropy production, linked to the absorption and emission of radiation, from material entropy production, which arises from turbulence, heat fluxes, and phase transitions. Evaluating these components provides insight into how energy is redistributed and degraded within the system, and how this affects overall climate dynamics. Results indicate that increasing greenhouse gas concentrations reshape the pathways of entropy generation. Enhanced retention of long-wave radiation strengthens internal dissipative processes, leading to higher material entropy production and shifting the balance between radiative and non-radiative contributions. These changes affect the efficiency of energy transport and influence large-scale circulation patterns. In addition, variations in entropy production offer a useful lens for examining climate sensitivity and stability. Increased internal irreversibility reflects modifications in feedback mechanisms and suggests potential movement toward altered equilibrium states. This thermodynamic approach helps clarify the fundamental constraints governing climate system responses to external forcing.

Quantitative Assessment of PSM Enhancements in Chemical Manufacturing

Mon, 05 Jan 2026 00:00:00 +0000

Process safety management (PSM) plays a crucial role in preventing major accidents in chemical plants. Despite established regulations and recognized best practices, many facilities continue to face challenges in maintaining effective safety systems. This study examines areas where PSM performance can be strengthened by identifying common gaps in risk evaluation, operational practices, equipment maintenance, worker competency, and emergency preparedness. Strengthening these aspects is essential for reducing the likelihood of catastrophic events in chemical operations. This study evaluates how targeted PSM improvements enhanced safety performance in three medium-sized chemical plants over two years. The focus was on improving process hazard analysis (PHA), management of change (MOC), mechanical integrity (MI), and workforce competency. The quantitative results show significant benefits: the use of improved hazard-identification tools and scenario-based PHAs reduced high-risk findings by 35%; digitalizing MOC processes lowered unauthorized changes by 42%; implementing predictive maintenance and equipment-health monitoring cut critical equipment failures by 28%; and enhanced worker training and competency checks led to a 22% decrease in human-error-related deviations. Overall, the plants experienced a 31% reduction in total incidents and a 46% increase in near-miss reporting, reflecting a stronger safety culture and greater employee involvement. These outcomes indicate that integrating data-driven tools with robust management practices and workforce support leads to meaningful improvements in PSM performance. The findings demonstrate that real, measurable safety advances are possible when technological and organizational enhancements are effectively combined in chemical plant operations.

Advanced Enterprise Resource Planning (ERP) Deployment Strategies: A Case Study on Optimizing Materials Management and Lifecycle Workflows in Modern Industry

U. K. Satpute, A. D. Gavande, D. B. Chauvan, D. A. Deshmukh, G. N. Deshpande — Wed, 03 Jun 2026 00:00:00 +0000

Modern corporate environments demand unified data handling to optimize manufacturing activities, streamline vendor ecosystems, and enable rapid strategic decisions. Enterprise Resource Planning (ERP) frameworks, particularly SAP, represent the highest standard of consolidated organizational architecture globally. This research presents a detailed study on the deployment, data management setup, and operational results of an Advanced ERP System. Using a mixed methodology that combines a semi-systematic literature review with an empirical site case study of an automotive sector component manufacturer (SMS1), they analyze system integration pathways. The implementation lifecycle is rigorously mapped across five core phases from initial Project Preparation, Business Blueprinting, and Realization to Final System Testing and Production Go-Live. The empirical observations focus on the explicit design of the organizational hierarchy under the SAP Implementation Guide (IMG) path, including Company Codes, local Plants, and individual Storage Locations (ROH1/FER1). This setup provides the required foundation for Master Data and transactional configurations. Functional performance was evaluated by tracing end-to-end procurement cycles via Transaction Codes such as ME21N, MIGO, and MIRO. The results show that deploying this advanced ERP layout eliminates cross-departmental data silos, establishes an organizational 'single source of truth', reduces inventory carrying costs through automated Material Requirements Planning (MRP), and secures transactional audit compliance. Ultimately, this study shows that a properly executed advanced ERP roadmap provides an essential strategic foundation for growth, enabling modern firms to scale efficiently and respond adaptively to changing global market demands.