Systematic evaluation of a full-scale textile wastewater treatment plant using the GPS-X and analytical measurements

Abstract

Extensive quantities of water and chemicals are used in the textile industry processes. Therefore, the treatment of textile wastewater is vital to protect the environment, maintain the public health, and recover resources. However, due to inadequate quality data, inexperienced plant operators, and inconsistent measurements, a prediction on the effluent quality of a textile wastewater treatment plant is difficult. Thus, the aim of this study was to comprehensively evaluate the full scale textile wastewater treatment plant using the GPS-X software and analytical measurements to establish the operational strategies for the plant. It also aimed to develop the troubleshooting strategies in a Bahir Dar textile factory, Ethiopia. Based on the stated aim, the research had the following four specific objectives. The first specific objective of the research was to characterize the wastewater physicochemical properties and evaluate the performance of the wastewater treatment plant in the textile factory. In the inlet and outlet of the wastewater treatment plant (WWTP), samples were collected for six months and analyzed on-site and in a laboratory for parameters including dissolved oxygen, pH, temperature, total Kjeldhal nitrogen (TKN), chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total suspended solids (TSS), total nitrogen (TN), total phosphorous (TP), nitrite, nitrate, and metallic compounds. These 22 parameters were classified into three functional groups: regulatory compliance (to assess legal discharge), nutrient dynamics (to monitor biological health), and model fractionation (to serve as specific mathematical inputs for the simulation). Statistical analyses were conducted using descriptive statistics and outlier analysis via SPSS to manage the stochastic nature of textile discharge; these methods conform to current environmental engineering trends, specifically identifying that effluent failures were operational rather than influent-driven. The results showed that the TSS, BOD5, COD, TP, nitrite, ammonia, and total chromium contents were above the discharge limit with the values of 73.2 mg/L, 48.45 mg/L, 144.08 mg/L, 7.9 mg/L, 1.36 mg/L, 1.96 mg/L, and 0.16 mg/L, respectively. The second objective was conducted to model, simulate, and optimize the operational process control parameters (DO setpoints, HRT, SRT, WAS, and RAS) under different scenarios. Scenario development is centered on a comparative analysis between the factory’s existing as-is physical layout and an optimized should-be digital model, with the primary criteria focused on aligning operations with scientifically accepted process flows through a highly regulated Conventional Activated Sludge (CAS) framework. By transitioning to this optimized state, the research establishes a performance benchmark designed to maximize pollutant removal efficiency while simultaneously achieving significant reductions in both energy consumption and overall operational costs. GPS-X was selected over similar software like BioWin or WEST due to its superior Carbon and Energy Footprinting modules and validated accuracy in full-scale industrial modelling. Two primary scenarios were developed based on a gap analysis approach: scenario I (Existing Layout), governed by the criterion of physical fidelity, and scenario II (Modified Process Flow), governed by scientific optimization. While limited to two structural layouts for comparative feasibility, true optimality was achieved within scenario II through thousands of digital iterations via dynamic sensitivity analysis on variables such as SRT and RAS. The model was calibrated using four-months’ measured data and validated and verified using two-months’ measured data. The results showed that the existing process model (scenario I) compared to the modified process model (scenario II) for TSS, COD, TP, and NO3, violated the compliance limit. However, the energy consumption and operation cost for scenario II were reduced by 52.9% and 56.98%, respectively. The third objective was conducted to evaluate the treatment plant performance using analytical measurement and GPS-X modelling software. While GPS-X supports dynamic simulation, this study utilized steady-state analysis to establish a robust operational baseline, as the lack of online sensors would have introduced significant synthetic noise into a dynamic model. The selection of steady-state modelling was a strategic decision necessitated by the absence of high-frequency historical data and the stochastic nature of textile batch-dyeing operations. In this regard, the pollutant removal efficiency results from analytical measurement and GPS-X model were 71% and 43%, respectively. The simulation results for scenario I showed that it was energy intensive, and indicated poor effluent quality, elevated operation costs, and high sludge production. However, scenario II was found to be the more efficient and a more effective treatment option compared to scenario I. The modelling-based performance evaluation technique was shown to be superior to the analytical measurements evaluation by identifying the parameter that violated the permissible limit, the duration of the violation, the mode of operation, and its location in the treatment plant. The fourth objective of this research was conducted to optimize key process control parameters to the observed operational challenges of existing processes, and to suggest an operational guide to the operators and decision makers to enhance the treatment performance in the GPS-X software. According to the formulated troubleshooting and decision support strategy, the optimization results of waste activated sludge in the primary and secondary clarifiers were within the range of 15 ± 5 m3/d and 83 ± 7 m3/d, respectively. In line with this, the recycled activated sludge flow was optimized to 150 ± 10 m3/d. The sludge retention time was found to be 5 ± 1d and 6.7 ± 0.5d in the secondary and primary clarifiers, respectively. In the GPS-X model, molasses addition represented as an increase in the readily biodegradable substrate fraction of the influent which contributed to save the mechanical energy for aeration by creating a layer of biochemical control instead.The addition of a carbon source, molasses resulted in a flow of 0.5 ± 0.05 m3/d, and the variation of influent was optimized to 600 ± 50 m3/d due to wastewater characteristics and rainfall. The optimum airflow into the aeration tank was 550 ± 5 m3/hr, which resulted in a 91.5% saving of energy in the optimized process. The solid mass flow production was reduced from 1087 kg/d (existing process) to 760 kg/d (optimized process), while the overall pollution load in the effluent was reduced from 260 kg/d (existing process) to 20 kg/d (optimized process). Consequently, the findings disclosed that the optimized process control parameters tested under different troubleshooting strategies reduced the energy consumption, increased the effluent quality, and reduced the pollution load compared to the existing process of the plant.