Thermal Optimalization with CFD Analysis and Real-Time Performance Identification in Briquette Ovens Using Modbus-Based Communication

Nurul Hiron; Nundang  Busaeri; Firmansyah Maulana Sugiartana  Nursuwars; Aceng  Sambas; Rachmadian  Wulandana

doi:10.37934/arnht.28.1.2742

Authors

Nurul Hiron Electrical Department, University of Siliwangi, Tasikmalaya, Indonesia
Nundang Busaeri Electrical Department, University of Siliwangi, Tasikmalaya, Indonesia
Firmansyah Maulana Sugiartana Nursuwars Informatic Department, University of Siliwangi, Tasikmalaya, Indonesia
Aceng Sambas Artificial Intelligence for Sustainability and Islamic Research Center (AISIR), Universiti Sultan Zainal Abidin, Campus Besut, Malaysia
Rachmadian Wulandana SUNY The State University of New York: New Paltz, NY, US

DOI:

https://doi.org/10.37934/arnht.28.1.2742

Keywords:

Oven, Briquette, CFD, Cradle, Modbus

Abstract

Briquette ovens that utilize biomass energy frequently face obstacles in achieving consistent heat distribution, resulting in ineffective energy usage and varying product quality. This study tackles these challenges by merging Computational Fluid Dynamics (CFD) analysis, performed with Cradle software, alongside a Modbus-based temperature data acquisition system to significantly boost energy efficiency and temperature regulation. A total of 32 K-type thermocouples were meticulously positioned within the oven to gather real-time temperature data, which was processed through LabVIEW. CFD simulations revealed irregular heat distribution patterns, especially at the rear of the oven. The temperature data obtained via Modbus corroborated these observations, allowing for strategic modifications to enhance heat distribution and minimize energy loss. The system achieved an impressive accuracy rate of 98.89%, with a mere error margin of 1.11%, substantially elevating the oven’s energy efficiency and briquette drying capabilities. This research clearly illustrates that the integration of CFD modeling with real-time data acquisition constitutes a powerful method for optimizing the functionality of industrial heating systems.

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Author Biographies

Nurul Hiron, Electrical Department, University of Siliwangi, Tasikmalaya, Indonesia

hiron@unsil.ac.id

Nundang Busaeri, Electrical Department, University of Siliwangi, Tasikmalaya, Indonesia

nundangb@unsil.ac.id

Firmansyah Maulana Sugiartana Nursuwars, Informatic Department, University of Siliwangi, Tasikmalaya, Indonesia

firmansyah@unsil.ac.id

Aceng Sambas, Artificial Intelligence for Sustainability and Islamic Research Center (AISIR), Universiti Sultan Zainal Abidin, Campus Besut, Malaysia

acengsambas@unisza.edu.my

Rachmadian Wulandana, SUNY The State University of New York: New Paltz, NY, US

wulandar@newpaltz.edu

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