Journal of Engineering in Industrial Research

Abstract Rapid urbanization in developing cities of Nigeria has led to significant environmental challenges, including the loss of green spaces and the degradation of ecosystem services. This review article explores the concept of sustainable smart urban form as a framework for integrating green spaces and ecosystem services into urban planning and development. The article highlights the importance of green spaces in urban planning, improving public health, and promoting environmental sustainability. The studies show that the integration of green spaces, such as parks, urban forests, and wetlands, can mitigate urban heat island effects, reduce air pollution, and support biodiversity. Additionally, ecosystem services, including water regulation, carbon sequestration, and recreational opportunities, contribute to the overall well-being of urban residents. However, challenges such as competing land-use priorities hinder the implementation of sustainable smart urban forms in Nigerian cities. Rapid urbanization in Nigeria has led to a significant increase in the number of people living in cities resulting in a growing focus on environmental protection and the integration of ecosystem services into urban planning frameworks.

Abstract The urgent need for water in industries and the large volume of wastewater discharge has prioritized wastewater treatment and water recycling in the research and operational projects of various public and private companies. In this context, Marun Petrochemical Company has established a treatment plant to recover water from its wastewater, aiming to not only provide part of its water needs through treated effluent, but also to reduce disposal costs and wastewater-related issues. This study uses a batch slurry photoreactor for laboratory-scale experiments. The statistical results of this study show that reducing pH and increasing catalyst concentration lead to higher Chemical Oxygen Demand (COD) removal. In addition, it was revealed that increasing the amount of co-oxidant results in further COD reduction at higher pH levels is attributed to a strong antagonistic interaction between the pH and co-oxidant parameters.

Abstract Airflow is an effective cooling solution for industries due to its abundance and accessibility. Utilizing air-cooled condensers aims to minimize water consumption in cooling towers by alleviating the heat load on turbine condensers. In this project, we simulated the C-3001 turbine system, E-4 condenser, and cooling tower by compiling data from the Meteorology Organization and relevant design specifications. After validating the accuracy of the simulated system, we applied operational conditions. The results revealed that the E-4 condenser faced a heat load exceeding its design capacity, potentially leading to complications within the system. Considering the site's physical conditions, equipment layout, and existing piping, a location was identified for branching the steam outlet from the steam guide hood to the condenser. We determined the maximum steam output and evaluated the feasibility of branching and its effects on turbine behavior through CFD analysis. The findings of this study indicated that condensing more than 20 tons per hour of the turbine's exhaust steam was not feasible. Consequently, we designed and analyzed a new condenser system using CFD. The results showed that this design could reduce the cooling tower's makeup water consumption by 16 cubic meters per hour in the methanol unit, while also positively impacting turbine efficiency and performance. Overall, this design effectively reduces the load on the E-4 condenser, subsequently lowering the turbine's exhaust pressure and enhancing its efficiency. The innovation of this work lies in identifying a solution that decreases the makeup water required for cooling, which could serve as a significant factor in improving turbine efficiency.

Abstract Machine Learning Operations (MLOps) refers to a set of practices and processes that aim to effectively integrate data science and machine learning in the production and support of enterprise-level applications and products. Distributed energy resources (DERs), such as photovoltaic (PV) panels, energy storage systems, and wind turbines, play a special role in making power grids smarter. These resources contribute to a more decentralized and resilient energy infrastructure, but the probabilistic nature of their energy production poses significant challenges for grid management. Data science provides powerful tools to address these challenges and optimize the operation and scheduling of DERs, to maximize their benefits and ensure grid stability. MLOps can help manage and optimize energy consumption by using machine learning algorithms to analyze energy consumption data and identify consumption patterns. Afterwards, by predicting consumption trends and identifying areas for improvement, energy consumption reduction strategies can be implemented. For example, in industries with high energy consumption, MLOps can help develop and deploy models that automatically adjust systems for greater efficiency and, as a result, reduce energy consumption. This could include adjusting building temperatures, optimizing manufacturing processes, or even better managing city traffic to avoid excessive fuel consumption. Ultimately, using MLOps, organizations can automate and continuously improve energy consumption optimization processes.

Abstract In solar energy applications, CH₃NH₃PbI₃ has offered a breakthrough in perovskite solar cell (PSC) research with different hole transport layers (HTL) and electron transport layers (ETL). This investigation studies CH₃NH₃PbI₃ PSC with SnO₂ as ETL and CuSCN or CuI as HTL. P₃HT and MEHPPV as an organic layer (Org) are inserted between CH₃NH₃PbI₃ and HTL. A simulation study is carried out on four combinations of SnO₂/CH₃NH₃PbI₃/Org/HTL/Au PSCs using SCAPS-1D. The current-voltage characteristics and energy bandgap of all the PSCs are discussed. The SnO₂/CH₃NH₃PbI₃/P₃HT/CuSCN/Au is the best PSC with Voc of 1.159 V, Jsc of 25.359 mA/cm², fill factor of 76.99% and a promising higher efficiency of 22.63% than others. The photovoltaic parameters of SnO₂/CH₃NH₃PbI₃/Org/HTL/Au are analyzed by varying the temperature, the thickness of the organic layers, and the bandgap of both the organic and inorganic HTLs to optimize the efficiency. Capacitance-voltage (C-V) plots are generated at various frequencies for all the proposed PSCs. The simulation results present a promising approach for the future design of highly efficient and stable PSCs.

Abstract There is the rapid evolution of autonomous systems, including autonomous vehicles, drones, and smart infrastructure, that has revolutionized industries through enhanced efficiency, precision, and scalability. It has, however, been accompanied by significant cybersecurity risks. Autonomous systems are susceptible to all types of cyber-attacks because of their connectivity to networks, including data tampering, sensor spoofing, denial-of-service attacks, and unauthorized access to control systems. These vulnerabilities stem from the integration of complex software architectures, communications networks, and sensor suites that offer a number of attack surfaces to malicious agents. This study provides a comprehensive assessment of the primary cybersecurity threats and vulnerabilities in autonomous systems, with an emphasis on their ability to affect safety, privacy, and operational integrity. Moreover, it analyzes state-of-the-art protection methods, including intrusion detection systems, encryption protocols, anomaly-based monitoring, and machine learning-based threat prevention methods. By presenting real case studies and current research advances, this study advocates for the urgent need for secure, adaptive, and multi-level security architectures for safeguarding autonomous systems against new and rising cyber threats. The findings stress the importance of coordination among cybersecurity researchers, system engineers, and policy makers to ensure the secure and dependable introduction of autonomous technologies into mission-critical applications.