Jinfeng Ma

Optimization of water quality monitoring network in a large river by combining measurements, a numerical model and matter-element analyses

A monitoring network that resolves the spatial and temporal variations of the water quality is essential in the sustainable management of water resources and pollution control. Due to cost concerns, it is important to optimize the monitoring locations so to use the least number of stations required to obtain the most comprehensive monitoring. The optimal design of monitoring networks is commonly based on the limited data available from existing measuring stations. The main contribution of this paper is the use of a numerical water quality model, calibrated with the available data. This model yields information on the water quality in any cross-section along the river, including the river reaches that are not monitored. Another contribution of the paper is the use of a matter-element analysis that allows for an objective division of the river in reaches that are homogeneous with respect to the water quality as assessed from multiple water quality parameters. The optimal monitoring network consists of one measuring station in each of these homogeneous reaches. The method has been applied to optimize the water quality monitoring network on the 1890 km long upper and middle reaches of the Heilongjiang River in Northeast China. The results suggest that the monitoring network improves considerably by relocating three stations, and not by adding extra stations.

Pipe break prediction based on evolutionary data-driven methods with brief recorded data

Pipe breaks often occur in water distribution networks, imposing great pressure on utility managers to secure stable water supply. However, pipe breaks are hard to detect by the conventional method. It is therefore necessary to develop reliable and robust pipe break models to assess the pipes probability to fail and then to optimize the pipe break detection scheme. In the absence of deterministic physical models for pipe break, data-driven techniques provide a promising approach to investigate the principles underlying pipe break. In this paper, two data-driven techniques, namely Genetic Programming (GP) and Evolutionary Polynomial Regression (EPR) are applied to develop pipe break models for the water distribution system of Beijing City. The comparison with the recorded pipe break data from 1987 to 2005 showed that the models have great capability to obtain reliable predictions. The models can be used to prioritize pipes for break inspection and then improve detection efficiency.

Optimizing the operation of the Qingshitan reservoir in the Lijiang River for multiple human interests and quasi-natural flow maintenance

For reservoir operation, maintaining a quasi-natural flow regime can benefit river ecosystems, but may sacrifice human interests. This study took the Qingshitan Reservoir in the Lijiang River as a case, and developed an optimization model to explore a trade-off solution between social-economic interests and nature flow maintenance on a monthly base. The objective function considered irrigation, cruise navigation and water supply aspects. An index of flow alteration degree was proposed to measure the difference between the regulated discharge and the natural flow. The index was then used as an additional constraint in the model besides the conventional constraints on reservoir safety. During model solving, different criteria were applied to the index, representing various degrees of alteration of the natural flow regime in the river. Through the model, a relationship between social-economic interests and flow alteration degree was established. Finally, a trade-off solution of the reservoir operation was defined that led to a favorable social-economic benefit at an acceptable alteration of the natural flow.

Improve the performance of full-scale continuous treatment of municipal wastewater by combing a numerical model and online sensors

Mathematical models based on instant environmental inputs are increasingly applied to optimize the operation of wastewater treatment plants (WWTPs) for improving treatment efficiency. This study established a numerical model consisting of the activated sludge module ASM3 and EAWAG bio-P module, and calibrated the model using data from a full-scale experiment conducted in a WWTP in Nanjing, China. The calibrated model was combined with online sensors for water temperature, chemical oxygen demand, NH+4-N and PO3-4-P to optimize and dynamically adjust the operation of the WWTP. The results showed that, compared to the original default operation mode, the effluent water quality was significantly improved after optimization even without supplementation of external carbon or alkalinity, and the required aeration rate in spring, summer, autumn, and winter was reduced by 15, 41, 33 and 11%, respectively. The study indicated that there was the potential for application of closed-loop automatic control to regulate operating parameters to improve wastewater treatment processes through the integration of data on influent characteristics and environmental conditions from sensors, and results from simulation models.