Structure validation of an integrated waterworks model for trihalomethanes simulation by applying regional sensitivity analysis

Uncertainty analysis of industrial energy conservation management in China's iron and steel industry

There are remarkable uncertainty factors in the industrial sector that enhance the difficulties of setting energy conservation strategies, such as the macro economy, industrial structures, and technical uncertainties. However, current studies simply predict the possible trends or conduct scenario analyses, and neglect uncertainty factors in the management of industrial energy conservation. In response, this article considers China's iron and steel industry as an example and builds the Industrial Energy Conservation Uncertainty Analysis (IECUA) model to recognize and analyze the uncertainty factors via a 200-thousand-time Latin hypercube sampling. Then, we propose some management measures, including setting energy conservation targets and energy conservation strategies. The results show that energy conservation targets should be more flexible than just the predicted values, to enhance the feasibility of their realization. In addition, energy conservation strategies are set at industrial and technique levels. On the one hand, such key parameters as production output, the coke/steel ratio, and pig iron/steel ratio, should be strictly controlled to avoid non-compliance risks. On the other hand, energy conservation technologies can be considered under four quadrants depending on their sensitivity to energy conservation and economic efficiency. Finally, some differentiated technologies promotion suggestions are made, such as economic stimulation, market entry standards and technical application guidelines.

Probabilistic ecological risk assessment of effluent toxicity of a wastewater reclamation plant based on process modeling

The growing use of reclaimed wastewater for environmental purposes such as stream flow augmentation requires comprehensive ecological risk assessment and management. This study applied a system analysis approach, regarding a wastewater reclamation plant (WRP) and its recipient water body as a whole system, and assessed the ecological risk of the recipient water body caused by the WRP effluent. Instead of specific contaminants, two toxicity indicators, i.e. genotoxicity and estrogenicity, were selected to directly measure the biological effects of all bio-available contaminants in the reclaimed wastewater, as well as characterize the ecological risk of the recipient water. A series of physically based models were developed to simulate the toxicity indicators in a WRP through a typical reclamation process, including ultrafiltration, ozonation, and chlorination. After being validated against the field monitoring data from a full-scale WRP in Beijing, the models were applied to simulate the probability distribution of effluent toxicity of the WRP through Latin Hypercube Sampling to account for the variability of influent toxicity and operation conditions. The simulated effluent toxicity was then used to derive the predicted environmental concentration (PEC) in the recipient stream, considering the variations of the toxicity and flow of the upstream inflow as well. The ratio of the PEC of each toxicity indicator to its corresponding predicted no-effect concentration was finally used for the probabilistic ecological risk assessment. Regional sensitivity analysis was also performed with the developed models to identify the critical control variables and strategies for ecological risk management.

Parameter Identification of an Ultrafiltration Model for Organics Removal in a Full-Scale Wastewater Reclamation Plant with Sparse and Incomplete Monitoring Data

Ultrafiltration (UF) has become one of the dominant treatment processes for wastewater reclamation in China. Modeling is an effective instrument to understand and optimize UF systems. To this end, a previously developed UF model for organics removal was applied to the UF process in a typical, full-scale wastewater reclamation plant (WRP) in China. However, the sparse and incomplete field monitoring data from the studied WRP made the traditional model analysis approaches hardly work in this case. Therefore, two strategies, namely Strategy 1 and Strategy 2, were proposed, following a regional sensitivity analysis approach, for model parameter identification. Strategy 1 aimed to identify the model parameters and the missing model input, i.e. sampling times, simultaneously, while Strategy 2 tried to separate these two processes to reduce the dimension of the identification problem through an iteration procedure. With these two strategies, the model performed well in the Qinghe WRP with the absolute relative errors between the simulated and observed total organic carbon (TOC) generally below 10%. The four model parameters were all sensitive and identifiable, and even the sampling times could be roughly identified. Given the incomplete model input, these results were encouraging and added to the trustworthiness of model when it was applied to the Qinghe WRP.