PAPER making! FROM THE PUBLISHERS OF PAPER TECHNOLOGY INTERNATIONAL ® Volume 8, Number 1, 2022
were obtained. For the ozonation process, an ozone flow of 2.5 g/h was fed to the wastewater, at an initial pH of 7.4. The effect of various ozone doses on COD, color and turbidity removal has also been studied. The highest achieved removal rates were 38.0%, 10.5% and 0.3% for COD, color and turbidity, respectively, at 20 min ozonation time. For the overall treatment, the removal percentages of these parameters were 90.9%, 99.7% and 99.9%, respectively. Furthermore, the combined treatment increased the wastewater biodegradability by a factor of 4.5. Thus, the combined processes of sedimentation, EC and ozonation can be a suitable alternative to reduce contaminant load in effluents from wood industries, and therefore, to comply with the Ecuadorian regulations. “ Water quality prediction model using Gaussian process regression based on deep learning for carbon neutrality in papermaking wastewater treatment system ”, Xin Wan, Xiaoyong Li, Xinzhi Wang, Xiaohui Yi, Yinzhong Zhao, Xinzhong He, Renren Wu & Mingzhi Huang, Environmental Research , Vol.211, Aug. 2022, 112942. Wastewater recycling is the measure with enormous potentiality to achieve carbon neutrality in wastewater treatment plants. High-precision online monitoring can improve the stability of wastewater treatment system and help wastewater recycling. A new water quality prediction CSWLSTM-GPR model, which fused the spatial feature of convolutional neural network (CNN), the temporal feature of sharing-weight long short-term memory (SWLSTM) and the probabilistic reliability of Gaussian process regression (GPR), was applied for monitoring papermaking wastewater treatment system with high-precision point prediction and interval prediction. Compared with SWLSTM-GPR and CLSTM-GPR, RMSE of CSWLSTM-GPR reduced by more than 48.9% on effluent chemical oxygen demand (CODeff), MAE reduced by more than 49.3%, R2 increased by more than 25.14%, R increased by more than 7.07%. And for the effluent suspended solids (SSeff), CSWLSTM-GPR had better predictive results than SWLSTM-GPR and CSWLSTM-GPR. Compared with SWLSTM-GPR, RMSE, MAE, R, R2 of CSWLSTM-GPR on effluent suspended solids (SSeff) were improved by 4.8%, 6.1%, 29.01% and 31.15%, respectively. Simulation results showed convincing comprehensive forecasting ability were obtained and the true values frequently stayed within the water quality range obtained by CSWLSTM-GPR model, which provided important insights for online monitoring, wastewater recycling and carbon neutrality of papermaking industry. “Evaluation of Discharged Untreated Wastewater from Pulping and Bleaching Oper ations in a Bamboo Based Tissue Paper Making Plant in Nigeria”, Akpan I. E. , Achadu M. Abah & Chukwuma F. O, Research and Analysis Journal , Vol.5(1), (2022). This study on Evaluation of Discharged Untreated Wastewater from Pulping and Bleaching operations in a Bamboo Based tissue Paper Making Plant in Nigeria was conducted mainly to evaluate the pollutant loads present in wastewater produced during the Pulping and Bleaching operations of the tissue paper making progression. The wastewater samples were obtained and examined to find out the pollutants they contain by checking the availability of some physicochemical parameters using standard methods by American Public Health Association (APHA). The analyzed parameters obtained for the pulping and bleaching processes respectively are pH (7.42 and 4.49), EC (316μS/cm and 47μS/cm), TDS (158mg/l and 24mg/l), Turbidity (19.3NTU and 28.7NTU), Temperature (28.47°C and 28.48°C), DO (3.66mg/l and 1.93mg/l), BOD5 (74.02mg/l and 62.00mg/l), COD (140.30mg/l and 120.40mg/l), TOC (10.3% and 7.1%), THB (1.60x103cfu/ml and 7.40x104cfu/ml), THF (0cfu/ml and 2.30x103cfu/ml) and heavy metals like Hg (<0.001mg/l and <0.001mg/l), Pb (0.281mg/l and 0.273mg/l), Ni (0.115mg/l and 0.117mg/l), Co (0.193mg/l and 0.208mg/l) and As (<0.001mg/l and <0.001mg/l). These results were also compared amid the permissible regulatory standards set by the FMEnv and NESREA.
Technical Abstracts
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