Chlorination is an effective disinfection method of drinking water treatment. Optimizing this process poses certain challenges, as the objectives of reaching the required disinfection effect and limiting the formation of disinfection by-products must be met simultaneously. These need adjusting the chlorine dosage in real time to cope with fluctuations in water quality and maintaining a free chlorine residual at the clear-water reservoir outlet that does not exceed regulatory limits. This paper proposes a composite control scheme that combines disturbance observer (DOB) and model predictive control (MPC) to meet the aforementioned objectives. MPC is employed for the feedback control of post-chlorine dosage, and DOB is adapted to monitor fluctuations in influent water quality. The estimated value from the DOB is applied in the feed-forward control of post-chlorine dosage. Both simulation and experimental results demonstrate that the proposed DOB-MPC composite control scheme provides a better disturbance rejection effect against fluctuations in influent water quality than MPC-only and PID-only control schemes, and it can effectively stabilize the quality of treated water.

Electricity consumption of pump group accounts for a significant proportion of cost in water distribution system (WDS). This paper discusses the optimal regulation method for parallel water-intake pump group consisting of fixed-frequency pumps and variable-frequency pumps. For this purpose, the regulation strategy for the water level of clean water tank is designed, and then the intake water flow of parallel pump group is calculated. For overall optimazing operation of parallel water-intake pump group, the mimimization of electricity consumption is performed within the constraints of limited pressure fluctuations of WDS and required flow of water supply. Dynamic programming algorithm and particle swarm optimization algorithm are employed to solve the optimization problem with constraints. Experimental studies have been conducted and the results show that 4.20% electricity-saving can be achieved by using the proposed optimal regulation method.

Many traffic occasions such as tunnels, subway stations and underground parking require accurate and continuous positioning. Navigation and timing services offered by the Global Navigation Satellite System (GNSS) is the most popular outdoor positioning method, but its signals are vulnerable to interference, leading to a degraded performance or even unavailability. The combination of magnetometer and Inertial Measurement Unit (IMU) is one of the commonly used indoor positioning methods. Within the proposed mobile platform for positioning in seamless indoor and outdoor scenes, the data of magnetometer and IMU are used to update the positioning when the GNSS signals are weak. Because the magnetometer is susceptible to environmental interference, an intelligent method for calculating heading angle by magnetometer is proposed, which can dynamically calculate and correct the heading angle of the mobile platform in a working environment. The results show that the proposed method of calculating heading angle by magnetometer achieved better performance with interference existence. Compared with the uncorrected heading angle, the corrected accuracy results could be improved by 60%, and the effect was more obvious when the interference was stronger. The error of overall positioning trajectory and true trajectory was within 2 m.