Xiang Zeng

Dynamic canal-system scheduling faces the fundamental challenge of determining the optimal reduction in the current period’s water allocation to reserve sufficient water for remaining periods, thereby hedging against potentially greater future water shortages. Although forecast information has been widely incorporated to address this hedging problem, its effectiveness is heavily dependent on forecast accuracy. Integrating abundant historical canal scheduling data with forecast information provides a promising pathway to improve scheduling performance, yet relevant studies remain limited. This study introduces the concept of Target Residual Lump-Sum Water Quota (TRLSWQ) for each time interval and develops a novel “Bi-level, Two-stage” (BT) model for dynamically updated canal-system scheduling that jointly leverages TRLSWQ and forecast information. The model defines clear canal scheduling rules and effectively adapts to the hierarchical structure in canal system scheduling. The model is applied to the summer–autumn irrigation scheduling of the Yongji main canal and six associated sub-canals in the Hetao Irrigation Area, Inner Mongolia, China. The results indicate that compared with the conventional model, the BT model reduces the total water shortage index of sub-canals from 40.81 to 31.44 (a decrease of 22.9%) and increases the utilization rate of the water quota from 89.3% to 92.9% (an increase of 3.9%). Furthermore, this study clarifies the mechanism of canal scheduling deviations caused by forecast errors: early-stage rainfall under-forecasting induces excessive early-stage allocation, leaving no water for later periods, whereas early-stage over-forecasting leads to withheld early allocation and unused residual lump-sum quota in later stages. The BT model effectively balances shortage risks between current and future periods and offers a practical and robust strategy for improving dynamic canal scheduling in irrigation districts.

In this paper, improved reinforcement learning (IRL) is designed for the multi-objective optimization operation of a cascade reservoir system. The primary improvement of IRL is searching within limited solution space, based on the derived monotonic property: the first-order derivative relationship between individual reservoir water release decisions for mainstream use (i.e., hydropower generation) as well as tributary use (i.e., regional water supply) and the cascade system’s or a particular reservoir’s water availability, along with the synchronicity and substitutability assumption of storage distribution in the cascade system. The improved algorithm is then applied to a real-world cascade reservoir system in the Yangtze River of China. The results demonstrate the high computational efficiency and reasonable interpretability of IRL.

As copper (Cu) and zinc (Zn) usually occur in livestock wastewater, this study focused on metals adsorption in struvite from swine wastewater and the release properties of granular struvite in solution with varying pH conditions (2, 4, 7). The results demonstrated pH values presented a slightly decreasing trend with increasing Cu/Zn ratio, and Zn exhibited higher adsorption performance on struvite crystals than that of Cu. Under the high content of metals in the wastewater, Cu/Zn ratios in the wastewater contributed to varying metal binding forms and mechanisms, resulting in the difference in the leaching properties of nutrients and metal. For the granular struvite manufactured with the adhesion of alginate, the P release percentage achieved 30.3-40.5% after 96 h in the wastewater of pH 2, whereas they were only 5.63-8.92% and 1.05-1.50% in the wastewater of pH 4 and 7, respectively. Acid wastewater contributed to the release of two metals, and the release amount of Zn was higher than that of Cu, which is associated with their adsorption capacity in crystals. During the latter soil leaching test of adding granular struvite, the NH₄⁺-N and PO₄^3--P concentration in the effluent ranged from 0.34-1.26 and 0.62-2.56 mg/L after 96 h, respectively. However, the Cu and Zn could not be measured due to lower than the detection limit under varying treatments. Struvite might be accompanied by quicker metal leaching and slower nutrient leaching when surface adsorption dominates in wastewater with lower metal concentrations.

It is hardly realistic to assume that, under all decision circumstances, followers will always choose a solution that leads to the worst upper-level objective functional value. However, this generally accepted concept of the pessimistic solution to the ill-posed bilevel programming problems may lead to the leader’s attitude being more pessimistic vis à vis his anticipation of the follower’s decision being non-antagonistic. It will result in a wrong pessimistic solution and a greater potential of cooperation space between the leader and the followers. This paper presents a new concept of a non-antagonistic pessimistic solution with four numerical examples for bilevel programming problems from a non-antagonistic point of view. We prove that the objective function value of the non-antagonistic pessimistic solution generally dominates or is equal to the objective functional value of the pessimistic solution and the rewarding solution, and the maximum potential space for leader-follower cooperation can be overestimated in a generally applied pessimistic solution. Our research extends the concept of the pessimistic solution. It also sheds light on the insights that the non-antagonistic pessimistic solution can describe the practical potential of cooperation space between the leader and followers in non-antagonistic circumstances.

Hedging is widely used to mitigate severe water shortages in the operation of reservoirs during droughts. Rationing is usually instituted with one hedging policy, which is based only on one trigger, i.e., initial storage level or current water availability. It may perform poorly in balancing the benefits of a release during the current period versus those of carryover storage during future droughts. This study proposes a novel hedging rule to improve the efficiency of a reservoir operated to supply water, in which, based on two triggers, hedging is initiated with three different hedging sub-rules through a two-step approach. In the first step, the sub-rule is triggered based on the relationship between the initial reservoir storage level and the level of the target rule curve or the firm rule curve at the end of the current period. This step is mainly concerned with increasing the water level or not in the current period. Hedging is then triggered under the sub-rule based on current water availability in the second step, in which the trigger implicitly considers both initial and ending reservoir storage levels in the current period. Moreover, the amount of hedging is analytically derived based on the Karush–Kuhn–Tucker (KKT) conditions. In addition, the hedging parameters are optimized using the improved particle swarm optimization (IPSO) algorithm coupled with a rule-based simulation. A single water-supply reservoir located in Hubei Province in central China is selected as a case study. The operation results show that the proposed rule is reasonable and significantly improves the reservoir operation performance for both long-term and critical periods relative to other operation policies, such as the standard operating policy (SOP) and the most commonly used hedging rules.