Rabika, Reval Dion
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OPTIMALISASI DAYA LISTRIK PADA ALAT AIR BLAST UNTUK MENINGKATKAN EFISIENSI OPERASIONAL LABORATORIUM OTOMASI INDUSTRI Rabika, Reval Dion; Rongkonusa, Kevin Marcelino; Budiman, Marson James
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 02 (2026): Volume 11 Nomor 02, Juni 2026 Published
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i02.54508

Abstract

The Air Blast machine at the Industrial Automation Laboratory of Politeknik Negeri Manado is equipped with two belt conveyors — one horizontal feed conveyor and one inclined discharge conveyor — each driven by a 0.75 kW three-phase induction motor. Under the conventional direct-on-line (DOL) start system, both motors run at constant maximum speed regardless of material load, resulting in unnecessary energy waste, a low system power factor of 0.68, and a high inrush current of 24.8 A at each startup. This study designs and evaluates an Arduino Nano-based control system integrated with a VEIKONG VFD to dynamically regulate conveyor belt speed based on real-time motor current data acquired via PZEM-004T sensors. The system employs a three-tier speed profile: full speed during material loading, reduced speed during processing, and minimum speed during idle periods, all managed autonomously by the Arduino Nano mounted on a NANO aPLC 5.2 expansion board. Measurements were conducted over 30 working days split into two 15-day phases. Daily energy consumption fell from 7.6 kWh to 5.3 kWh (a 30.3% reduction), the system power factor improved from 0.68 to 0.77, reactive power decreased by 45.6%, and the start inrush current dropped from 24.8 A to 7.4 A (a 70.2% reduction). At the laboratory system level, total daily consumption decreased from 18.0 kWh to 15.7 kWh, a 12.7% improvement. Estimated monthly cost savings amount to Rp 73,122, with a project payback period of under 26 months against a total investment of Rp 1,850,000.