Aiming at the minimum power consumption for water pumping in a cascade pumping station system, we established a complex nonlinear mathematical model by taking optimized lift head and blade angle (or speed) of pump units as decision variables. The on-site optimal operation mode conforming to single-station water restriction was obtained by water decomposition–dynamic programming aggregation solution of the optimal operation model of pump stations under different discrete heads. After that, the constraint of total system lift head was considered. By taking lift heads of pump stations as decision variables, the original model was transformed into a one-dimensional dynamic programming aggregation model, which was solved by dynamic programming method. Through numerical simulation of unsteady flow in an interstage water channel, the optimal head distribution was modified to obtain optimal head distribution and unit optimization operation modes of pumping stations.

Greenhouse gas (GHG) emissions, which are closely related to climate change and serious ecological instability, have attracted global attention. The estimation of crucial aquatic factors for the flux of GHGs in lakes is a key step in controlling and reducing GHG emissions. The importance of 14 aquatic factors for GHG emissions was estimated in Meiliang Bay, which is an eutrophication shallow bay in Taihu Lake in eastern China. The random forest (RF) method, which is an improved version of the classified and regression tree (CART) model, was employed. No distribution assumption on variables was required in this method and it could include nonlinear actions and interactions among factors. The results show significant positive correlations among the fluxes of CO₂, CH₄, and N₂O. The most crucial factor influencing CO₂ emissions is the water temperature (WT) followed by sulfate (SO₄²⁻), alkalinity (Alk), dissolved oxygen (DO), and nitrate (NO₃⁻–N). The important factors for CH₄ emissions are WT, SO₄²⁻, DO, Alk, and NO₂⁻–N. The outcome for N₂O, in which the key factor is NO₂⁻–N, was slightly different from those of CO₂ and CH₄. A comprehensive ranking index (CRI) for the fluxes of all three GHGs was also calculated and showed that WT, NO₂⁻–N, SO₄²⁻, DO, and Alk are the most crucial aquatic factors. These results indicate that increasing DO might be the most effective means of controlling GHG emissions in eutrophication lake bays. The role of SO₄²⁻ in GHG emissions, which has previously been ignored, is also worth paying attention to. This study provides a useful basis for controlling GHG emissions in eutrophication shallow lake bays.

Accelerated urbanization places extraordinary demands on sewer networks; thus optimization research to improve the design of these systems has practical significance. In this article, a subsystem nonlinear programming model is developed to optimize pumping station layout and sewage pipe network design. The subsystem model is expanded into a large-scale complex nonlinear programming system model to find the minimum total annual cost of the pumping station and network of all pipe segments. A comparative analysis is conducted using the sewage network in Taizhou City, China, as an example. The proposed method demonstrated that significant cost savings could have been realized if the studied system had been optimized using the techniques described in this article. Therefore, the method has practical value for optimizing urban sewage projects and provides a reference for theoretical research on optimization of urban drainage pumping station layouts.