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All Journal Jurnal Teknik Industri : Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri Green Engineering: Journal of Engineering and Applied Science
Riza Phahlevi Marwanto
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering

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AI driven Circular Waste to Energy Conversion System Using Smart Thermal Monitoring and Emission Optimization for Sustainable Urban Infrastructure Kiki Ahmad Baihaqi; Krisna Widi Nugraha; Rian Ardianto; Rosyid Ridlo Al-Hakim; Riza Phahlevi Marwanto; Erick Fernando
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.289

Abstract

This study explores the integration of Artificial Intelligence (AI) with thermal optimization in Waste-to-Energy (WtE) systems to enhance both energy recovery and emission control. Introduction: The growing need for sustainable urban waste management has highlighted the importance of optimizing WtE systems. AI technologies, including machine learning and deep learning, have shown potential in improving the efficiency of WtE processes, especially in reducing emissions and enhancing energy recovery. Literature Review: Previous research indicates that AI has been successfully applied to various WtE technologies such as pyrolysis, gasification, and incineration, yet the integration of AI specifically for thermal optimization remains underexplored. Most studies focus on predictive models for emission reduction rather than real time thermal optimization. Materials and Method: The study proposes the development of an AI-driven framework that integrates real time data collection from IoT sensors, predictive modeling, and real time control algorithms. The system optimizes key parameters such as combustion temperature and fuel flow to enhance energy recovery and minimize emissions. The method includes data collection from operational WtE plants, followed by model development using machine learning algorithms. Results and Discussion: Initial simulations and pilot testing showed significant improvements in energy efficiency and emission reduction. AI-driven systems outperformed conventional WtE systems by optimizing operational parameters in real time. The study identifies gaps in AI integration for thermal optimization and suggests future research directions, including the integration of AI with smart grids and carbon credit systems for more sustainable WtE operations.
Ergonomic Analysis and Redesign of Bus Handgrips Using Rapid Upper Limb Assessment (RULA) and Posture Evaluation Index (PEI) Veronika Chintia Dewi; Rifano Rifano; Gunawan Gunawan; Riza Phahlevi Marwanto
JTI: Jurnal Teknik Industri Vol 12, No 1 (2026): Juni 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jti.v12i1.39493

Abstract

This study aims to analyze and improve the ergonomic performance of bus handgrips on Metro Jabar Trans using an integrated approach combining Rapid Upper Limb Assessment (RULA) and Posture Evaluation Index (PEI). The research employed a quantitative method involving 30 respondents, with data collected through observation, interviews, joint angle measurements, and anthropometric analysis. A virtual simulation was developed using Jack 8.4 software to evaluate ergonomic conditions based on RULA, Lower Back Analysis (LBA), and Ovako Working Posture Analysis System (OWAS), which were then integrated into the PEI metric. The results indicate that the existing handgrip design produces RULA scores predominantly at levels 5–6, reflecting moderate ergonomic risk, with PEI values ranging from 1.358 to 1.875, indicating suboptimal conditions. A redesigned handgrip was developed by optimizing key dimensions, including width, diameter, and finger clearance, based on anthropometric data. Simulation results demonstrate a consistent reduction in ergonomic risk, with PEI values decreasing to a range of 0.925 to 1.221. The reduction in PEI values, ranging from 0.233 to 0.903, confirms improved postural conditions and reduced risk of musculoskeletal disorders. These findings highlight that integrating posture assessment and quantitative ergonomic indices can effectively support the development of safer and more comfortable public transportation facilities.
Co-Authors Baihaqi, Kiki Ahmad Erick Fernando Gunawan Gunawan Krisna Widi Nugraha Rian Ardianto Rifano Rifano Rosyid Ridlo Al-Hakim Veronika Chintia Dewi