Jianping Bing

Bio: Jianping Bing is an academic researcher from Changjiang Water Resources Commission. The author has contributed to research in topics: Ecosystem health & Water resources. The author has an hindex of 1, co-authored 1 publications receiving 14 citations.

Critical review of system dynamics modelling applications for water resources planning and management

Abstract: This paper systematically reviewed system dynamics applications in water resource management with respect to spatial factors, research aims, modelling sub-systems and model calibration and validation methods. Decision-making context, consideration of climate change, scenarios or management measures were also examined. Results showed that the critical conceptual system dynamics model development phases are often neglected with only 40% of reviewed articles developing causal loop diagrams, and only three studies identifying system archetypes. Most reviewed publications applied scenario-based approaches (82%) to evaluate the effectiveness of management measures, whilst a minority of studies (1.8%) considered water management optimisation. Structure behaviour and behaviour-pattern tests (52%) were mostly applied to evaluate the validity of the model structure and accuracy of the behaviour's pattern, though model validation was not conducted in 12% of the studies. Finally, an integrated system dynamics modelling framework was proposed, exploring opportunities for integration of system dynamics with other modelling tools based on the strengths and limitations of system dynamics modelling; this framework can be used to enhance the simulation and optimisation for future water resource management and planning in the context of climate change and socio-economic development.

Modeling and Optimizing of NH4+ Removal from Stormwater by Coal-Based Granular Activated Carbon Using RSM and ANN Coupled with GA

Abstract: As a key parameter in the adsorption process, removal rate is not available under most operating conditions due to the time and cost of experimental testing. To address this issue, evaluation of the efficiency of NH4+ removal from stormwater by coal-based granular activated carbon (CB-GAC), a novel approach, the response surface methodology (RSM), back-propagation artificial neural network (BP-ANN) coupled with genetic algorithm (GA), has been applied in this research. The sorption process was modeled based on Box-Behnben design (BBD) RSM method for independent variables: Contact time, initial concentration, temperature, and pH; suggesting a quadratic polynomial model with p-value pH (24.68%) > temperature (22.93%) > initial concentration (21.16%). The process parameters were optimized via RSM optimization tools and GA. The results of validation experiments showed that the optimization results of GA-ANN are more accurate than BBD-RSM, with contact time = 899.41 min, initial concentration = 17.35 mg/L, temperature = 15 °C, pH = 6.98, NH4+ removal rate = 63.74%, and relative error = 0.87%. Furthermore, the CB-GAC has been characterized by Scanning electron microscopy (SEM), X-ray diffraction (XRD) and Brunauer-Emmett-Teller (BET). The isotherm and kinetic studies of the adsorption process illustrated that adsorption of NH4+ onto CB-GAC corresponded Langmuir isotherm and pseudo-second-order kinetic models. The calculated maximum adsorption capacity was 0.2821 mg/g.