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Green Engineering: Journal of Engineering and Applied Science

GreenEngineering
Website

Published by International Forum of Researchers and Lecturers

ISSN : 30636841 EISSN : 30636833 DOI : 10.70062

Core Subject : Agriculture, Engineering,

Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering

(Green Engineering: Journal of Engineering and Applied Science) [e-ISSN : 3063-6833, p-ISSN : 3063-6841] is an open access Journal published by the IFREL ( Forum of Researchers and Lecturers). Green Engineering accepts manuscripts based on empirical research results, new scientific literature review, and comments/ criticism of scientific papers published by Green Engineering. This journal is a means of publication and a place to share research and development work in the field of Engineering and Applied Science. Articles published in Green Engineering are processed fully online. Submitted articles will go through peer review by a qualified international Reviewers. Complete information for article submission and other instructions are available in each issue. Green Engineering publishes 4 (four) issues a year in January, April, July and October, however articles that have been declared accepted will be queued in the In-Press issue before published in the determined time.

Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)

Articles 42 Documents

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Sustainable Precision Agriculture Irrigation System Using Edge Computing and Renewable Energy Integration for Water Conservation and Climate Adaptation Agus Wantoro; Ferly Ardhy; Fahlul Rizki; Ahmad Budi Trisnawan; Yulaikha Mar’atullatifah; Rachmat Setiabudi
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.v2i2.288

The integration of solar powered IoT irrigation systems in precision agriculture offers a sustainable solution to address water scarcity and enhance crop productivity. By leveraging real time data from soil sensors, weather APIs, and machine learning algorithms, these systems optimize irrigation schedules and improve water use efficiency. This research explores the potential of integrating renewable energy sources, such as solar power, with edge computing in smart irrigation systems to promote sustainable agricultural practices. The study aims to evaluate the performance of the proposed system in terms of water savings, crop yield, energy efficiency, and adaptability to varying climate conditions. Literature Review: Previous studies highlight the importance of smart irrigation systems in reducing water waste and improving crop yield through real time monitoring and automated decision making. However, existing systems often lack the integration of renewable energy and edge computing, which are critical for ensuring sustainability and operational efficiency in rural agricultural settings. The combination of renewable energy with IoT devices offers a promising solution to reduce energy costs and carbon emissions, while edge computing enhances real time data processing, ensuring prompt and accurate irrigation adjustments. Materials and Method: The proposed system integrates solar powered IoT devices, soil moisture sensors, weather data APIs, and edge computing devices to manage irrigation. Machine learning algorithms and evapotranspiration models are used to predict irrigation needs and optimize scheduling based on real time data. The system's performance is evaluated through metrics such as water savings percentage, crop yield improvements, and energy consumption, with a comparative analysis against traditional irrigation methods. Results and Discussion: The results indicate that the system successfully reduces water usage by 30% to 40%, increases crop yield by 25%, and operates with energy autonomy, powered entirely by solar energy. The system's adaptability to varying climate conditions ensures optimal crop growth, even under environmental stresses. The integration of renewable energy and edge computing significantly enhances the sustainability and efficiency of irrigation systems.

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

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.

Page 5 of 5 | Total Record : 42

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Editorial Team

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Reviewer

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Contact Name
Abdul Karim

Contact Email
indexsasi@apji.org

Phone
+6282135809779

Journal Mail Official
info@ifrel.org

Editorial Address
Jalan Watunganten 1 No 1-6, Batursari, Mranggen, Kab. Demak, Provinsi Jawa Tengah, 59567

Location
Kab. demak,

Jawa tengah

INDONESIA

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Abdul Karim

Contact Email indexsasi@apji.org

Phone +6282135809779

Journal Mail Official info@ifrel.org

Editorial Address Jalan Watunganten 1 No 1-6, Batursari, Mranggen, Kab. Demak, Provinsi Jawa Tengah, 59567

Location Kab. demak, Jawa tengah INDONESIA

Abstract

Abstract

Contact Name
Abdul Karim

Contact Email
indexsasi@apji.org

Phone
+6282135809779

Journal Mail Official
info@ifrel.org

Editorial Address
Jalan Watunganten 1 No 1-6, Batursari, Mranggen, Kab. Demak, Provinsi Jawa Tengah, 59567

Location
Kab. demak,

Jawa tengah

INDONESIA