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The study intended to devise a sustainable treatment scheme for acrylic fiber dyeing process effluents, which significantly differ from other textile plant wastewaters. The necessary experimental support was generated from a set of tests conducted on the wastewater of a plant operation, focused solely on acrylic fiber dyeing. Related experiments involved effluent characterization, biological treatability, and chemical settling/oxidation performed on raw and bio‐treated effluent, as pre‐treatment and/or polishing steps. A total COD level of 910 mg/L with a 70% soluble ratio, low total N and P contents below 10 mg/L, a detergent content of 50 mg/L and a dark/black color of 1150 Pt‐Co unit defined the average properties of the raw plant effluent. Experimental support indicated that activated sludge operation at a sludge age level above 25 d, remained insufficient as it could only provide an effluent with a soluble COD level above 150 mg/L, a biomass escape in the range of 40 to 100 mg/L and with an intense dark color. A similar experimental template, including chemical settling, chemical oxidation by NaOCl and combination of the two processes, was tested as pre‐treatment and a polishing step after biological treatment. Pre‐treatment, although with some benefit to remove toxic and inhibitory compounds, proved to be highly costly, due to excessive chemical usage and sludge generation for a sustainable, cost‐effective approach. The sequence of chemical oxidation and settling steps implemented as a polishing stage was quite effective as it secured complete color removal, a major source of concern for the selected wastewater and also, it reduced COD down to 60 mg/L, a level significantly below the residual COD threshold for biological treatment. This approach, although case‐specific for this study, provided a scientific and general perspective for similar work. [ABSTRACT FROM AUTHOR]

BACKGROUND: The paper investigated the negative impact of rainwater intrusion and mixing with sewage, on the operation of an existing nutrient removal activated sludge plant. It evaluated the magnitude of observed changes, both in terms of flow increase and modification of major parameters, together with the resulting impact on system performance. The study was carried out at a selected plant. The influent characteristics of the plant were reviewed for 2021 and 2022, both for the dry and rainy periods. Collected data were evaluated together with the experimental support derived from the selected plant. RESULTS: Statistical assessment of the collected data revealed significant changes likely to affect the performance of the existing plant: Sewage flow was increased from 580 000 to 670 000 m3 day−1. Although chemical oxygen demand and total nitrogen were slightly reduced, a higher level of inorganic solids was observed. Assessment of nitrification–denitrification processes yielded an aerobic sludge age, θXA of 9 days and a total sludge age, θX of 15 days, with a VD:VT ratio of 0.4. Solids mass balance indicated a biomass level of 6100 mg L−1 with 47% of fixed solids, which was increased to 7300 mg L−1 with 52% fixed solids with the wet weather flow. These values denoted reactor volume limitation for sustainable/safe operation. CONCLUSION: Aside from the impact of substantial treatment cost increase resulting from the incremental flow of 13.7 million m3 year−1 during wet weather periods, control and decrease of inorganic solids emerged as the key issue to safeguard system operation. The study also indicated clues for diverting runoff mixing as a tangible remedial action. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Modeling of pan evaporation (Ep) is of paramount importance in the evaluation of drinking water supplies, planning of regional water resources and reservoir management. The main aim of this study is to investigate the accuracy of linear and non-linear ensemble approaches for monthly Ep modeling in Erbil and Salahaddin meteorological stations of Iraq. For this purpose, sensitivity analysis was performed to determine the dominant input parameters. The results showed that Tmean, Tmax and Tmin are the most effective parameters. Thereafter, two scenarios were involved for the Ep modeling. In scenario 1, the ability of artificial neural network, least-squares support-vector machine and multiple linear regression models was examined for the estimation of Ep. The results demonstrated that different input combinations led to different performance, model 3 (which has Tmean, Tmax, Tmin, RH) for Erbil station and model 2 (which has Tmean, Tmax, Tmin) for Salahaddin station provided the best performance among several models developed. In scenario 2, linear and non-linear ensemble approaches were employed as simple linear average, weighted linear average and non-linear ensemble (NLE) models to improve predictions of the single models. The results reported that ensemble modeling could improve performance of single models and NLE model provided the best results due to its non-linear nature. The general results demonstrated that the proposed ensemble models could improve predictions of single models up to 5% and 16% for Erbil and Salahaddin stations, respectively. [ABSTRACT FROM AUTHOR]