Iranian Research Organization for Science and Technology Advances in Environmental Technology 2476-6674 12 3 2026 07 01 Optimization of fermented rice noodle wastewater treatment using effective microorganisms (EM) 274 290 1676 10.22104/aet.2026.7571.2125 EN Tiammanee Rattanaweerapan Department of Chemical and Environmental Engineering, Faculty of Engineering, Ubon Ratchathani University, 34190, Ubonratchathani, Thailand Karnika Ratanapongleka Department of Chemical and Environmental Engineering, Faculty of Engineering, Ubon Ratchathani University, 34190, Ubonratchathani, Thailand 0000-0002-9663-503X Supatpong Mattaraj Department of Chemical and Environmental Engineering, Faculty of Engineering, Ubon Ratchathani University, 34190, Ubonratchathani, Thailand Wipada Dechapanya Department of Chemical and Environmental Engineering, Faculty of Engineering, Ubon Ratchathani University, 34190, Ubonratchathani, Thailand Sompop Sanongraj Department of Chemical and Environmental Engineering, Faculty of Engineering, Ubon Ratchathani University, 34190, Ubonratchathani, Thailand Journal Article 2025 04 19 The treatment of high-strength wastewater generated from fermented rice noodle production poses significant environmental challenges due to its elevated organic load, acidity, and nitrogen content. This study investigated the optimization of wastewater treatment using Effective Microorganisms (EM), focusing on the effects of initial wastewater pH (6–8) and EM dosage (1–10% v/v) on Chemical Oxygen Demand (COD) and Total Kjeldahl Nitrogen (TKN) removal efficiency. A Central Composite Design (CCD) within the framework of Response Surface Methodology (RSM) was employed to model and analyze the interactive effects of these operating parameters. The results demonstrated that near-neutral pH (6.9) and a low EM dosage (1.2% v/v) yielded the highest COD removal efficiency (80.21%), whereas an alkaline pH (8.0) with a low EM dosage (1% v/v) resulted in the maximum TKN removal efficiency (75.18%). Statistical analysis revealed that EM dosage significantly impacted COD removal (p < 0.0001), while initial pH had a more pronounced effect on TKN removal (p < 0.0001). The quadratic regression model exhibited strong predictive performance for both COD (R² = 0.9827) and TKN (R² = 0.9326) removal. The findings further indicate that COD removal is predominantly governed by biologically regulated microbial metabolism, whereas TKN removal is controlled mainly by pH-driven physicochemical pathways. Overall, the EM application optimized through RSM represents a promising and sustainable strategy for enhancing the simultaneous removal of organic matter and nitrogen from wastewater generated by the fermented rice noodle industry. https://aet.irost.ir/article_1676_831c2f88a604a07ca94314b56a4921b8.pdf