PAPER making! g! FROM THE PUBLISHERS OF PAPER TECHNOLOGY INTERNATIONAL ® Volume 11, Number 2, 2025
WASTE TREATMENT “Optimizing papermaking wastewater treatment by predicting effluent quality with node-level capsule graph neural networks”, G. Baskar, A.N. Parameswaran & R. Sathyanathan, Environmental Monitoring and Assessment , Vol.197, article number 176, (2025). Papermaking wastewater consists of a sizable amount of industrial wastewater; hence, real-time access to precise and trustworthy effluent indices is crucial. Because wastewater treatment processes are complicated, nonlinear, and time-varying, it is essential to adequately monitor critical quality indices, especially chemical oxygen demand (COD). Traditional models for predicting COD often struggle with sensitivity to parameter tuning and lack interpretability, underscoring the need for improvement in industrial wastewater treatment. In this manuscript, an optimized papermaking wastewater treatment method is proposed that predicts effluent quality using node-level capsule graph neural networks (PWWT-PEQ-NLCGNN). To improve the accuracy of predicting important effluent COD quality indices, the NLCGNN weight parameters are optimized using the hermit crab optimization (HCO) algorithm. The performance of the proposed PWWT-PEQ-NLCGNN technique demonstrated improvements over existing techniques. Specifically, the proposed strategy achieved 30.53%, 23.34%, and 32.64% higher accuracy; 20.53%, 25.34%, and 29.64% higher precision; and 20.53%, 25.34%, and 29.64% higher sensitivity compared to the water quality prediction model using Gaussian process regression based on deep learning for carbon neutrality in papermaking wastewater treatment system (WQP-GPR-DL- CLPWWTS), the prediction of effluent quality in papermaking wastewater treatment processes using dynamic kernel-based extreme learning machine (POEQ-PWWTP- DKBELM), and the quality-related monitoring of papermaking wastewater treatment processes using dynamic multi-block partial least squares (QRM-PWWTP-DMPLS). These results highlight the potential of the PWWT-PEQ-NLCGNN method for enabling timely and accurate monitoring of wastewater treatment processes. “CaCO 3 accumulation in the wastewater treatment system of a recycled paper mill with zero-liquid discharge”, Qingsi Song, Qiang Ge, Xianbo Zhou, Yujia Wang, Guifang Liu, Hefang Liu, Congyun Zhu, Kuanyong Liu & Shucheng Yang, Water Environmental Research , Vol.97, Issue6, June 2025, e70094. The accumulation of CaCO 3 caused by wastewater recycling is a common problem in recycled paper mills. This study analyzed the process water, wastewater and sludge characteristics of a zero-liquid- discharge recycled paper mill to explore the sources of calcium and the impacts of CaCO 3 precipitation. The results showed that the primary factor responsible for CaCO 3 dissolution during water closed recirculating was the pH reduction resulting from volatile fatty acids (VFA) generated through organic matter hydrolysis and acidification. VFAs, functioning as weak organic acids, participate in neutralization reactions with CaCO 3 , thereby enhancing its dissolution. Each ton of wastepaper could introduce about 5 kg calcium into wastewater. Ca 2+ was mainly removed in anaerobic process through CaCO 3 precipitation, which mainly accumulated in the granular sludge core, and caused the decrease of sludge VSS/TSS. Sludge with low VSS/TSS located at the bottom and the top of the reactors. The ash content of sludge at the bottom of the reactors reached more than 70%, and CaCO 3 accounted for over 55% of the total anaerobic sludge. The calculation of chemical equilibrium model and water stability showed that the effluent of the anaerobic reactors still had a strong tendency towards scaling, although 50% of Ca 2+ was removed. These findings highlight the importance of managing calcium levels in zero-liquid discharge paper mills to optimize wastewater treatment and prevent operational issues.
Technical Abstracts
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