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In this paper, within the framework of increasing the contributions to sustainable development goals and reducing the water footprint, the sustainable production potential of a factory producing denim fabrics have been studied in association with the sustainable development goals. For this purpose, Life Cycle Assessment and Material Input per Service methods were used to determine the environmental impact factors of the factory and the existing water footprint. Calculations were made in three different ways, taking the factory’s total production capacity, a selected product, and the wet processes into account. Although the sustainable production potential of the factory is demonstrated with the Sustainable Development Goals, it has been determined that the contribution rates differ according to both the calculation method and the production data taken into account. As a result of the evaluations, it has emerged as a more dominant view that the factory’s contribution to the Sustainable Development Goals should be evaluated according to the total production capacity. The sustainability evaluation made according to the total production capacity determined that the factory contributed approximately 12% to Sustainable Development Goal 12 in the period examined, according to both Life Cycle Assessment and Material Input per Service methods. Although there is inconsistency in the Life Cycle Assessment and Material Input per Service method results, it was predicted that there are economic and environmental gain potentials related to Sustainable Development Goals 13, 14, and 15, and the sustainable production potential of the factory can be increased.

The Fenton and photo‐Fenton oxidation processes (FOP and PFOP) are usually applied as a secondary unit process, and direct usage of both processes is critical in textile wastewater treatment. There is seldom study on the direct application of the FOP or PFOP showing the treatment of raw textile industry wastewaters. This study demonstrates the application and comparison of both FOP and PFOP as single units separately for the treatment of wastewater in a textile industry producing woven fabrics. In both processes, the highest treatment efficiency was achieved at pH 3. Chemical oxygen demand (COD), suspended solids (SS), and color parameters in FOP reduced from 1341 to 254 mg/L, 99.5 to 19.9 mg/L, and 1396 to 97.7 Pt‐Co, respectively. Separately, in the PFOP, 365‐nm wavelength UV radiation sources have been used. In PFOP, the same parameters were reduced from 715 to 42.9 mg/L, 90 to 9 mg/L, and 2080 to 83.2 Pt‐Co, respectively. These results were obtained at 0.7 g Fe2+/L and 2 mM H2O2 concentrations in both studies. PFOP can meet the textile industry receiving environment discharge standards of many countries, especially in Turkey. The use of PFOP as a single unit is possible in the treatment of textile industry wastewater without primary precipitation. The findings in this study may be practical for the adaptation of the processes on the field scale. Practitioner Points There is seldom study on the direct application of Fenton or photo‐Fenton processes as a single unit to raw textile wastewaters This study shows the application of the Fenton or photo‐Fenton processes as single units for the treatment of raw wastewater in a textile industry Results of both processes in this study meet the discharge standards of many countries Evaluations of efficiencies of both processes were achieved This study may be the focus of attention of treatment plant operators and researchers

Studies on the direct application of the photo-Fenton process (PFOP) to disinfect and decontaminate textile wastewater are rare. The output of the artificial neural network (ANN) models applied to the wastewater of a textile factory producing woven fabrics, which is used to assess the efficiency of the PFOP process, are investigated and compared with each other in this study. The highest PFOP efficiency is obtained at a pH of 3. Chemical oxygen demand (COD), suspended solids (SS) and color removal rates are 94%, 90%, and 96%, respectively. The data are modeled with ANNs and nonlinear external input autoregressive ANNs (NARX-ANN) using the MATLAB R2020a software program. Both Levenberg-Marquardt (trainlm) and scaled conjugate gradient (trainscg) algorithms are employed in the ANN and NARX-ANN models, whereas hyperbolic tangent sigmoid (Tansig) and logistic sigmoid (Logsig) functions are superimposed on the hidden layer in the ANN model, and Tansig functions are superimposed on the NARX-ANN model. It is determined that the developed ANN models are more effective in estimating the PFOP efficiency. The mean squared error is 0.000 953, and the coefficient of determination (R 2 ) is 0.96 661.