AI-Driven Deployable Water Quality Management: Benchmarking Effluent BOD Prediction at a European Pulp and Paper Mill

The pulp and paper industry consumes 10–300 m3 of water per ton of product and generates substantial wastewater
volumes. These facilities collect extensive operational data that
remain underutilized for treatment optimization. However, systematic
long-horizon comparisons of multiple machine learning algorithms for
effluent prediction under full-scale operating conditions remain scarce.
Using 1,033 daily records from a full-scale European mill (January
2020–October 2022), we evaluated five machine learning models
to predict effluent biochemical oxygen demand (BOD) for environmental
compliance monitoring. Input variables included flow rate, influent
total suspended solids (TSS), influent BOD, temperature, aeration
rate, hydraulic retention time, and chemical dosing. We benchmarked
support vector machines (SVMs), artificial neural networks (ANNs),
genetic programming (GP), decision trees (DTs), and random forests
(RFs). SVM achieved the best performance (R2 = 0.85, NSE
= 0.82, RMSE = 5.5 mg/L), while ANN delivered competitive accuracy
(R2 = 0.82, RMSE = 6.8 mg/L) with the fastest runtime.
GP produced interpretable mathematical equations, and DT/RF provided
high operational transparency despite lower accuracy. These models
can inform operational decisions related to aeration control, chemical
dosing, and compliance management. By combining routinely monitored
process variables with laboratory-measured influent BOD, our models
demonstrated strong retrospective predictive performance across a
three-year data set. This work establishes a benchmark for pulp and
paper wastewater management and clarifies pathways for integrating
real-time sensors to enable predictive process control.