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Recycling salt and water from saline wastewater is crucial for environmental preservation, resource conservation, and social economy sustainability. The heat pump air circulation evaporating separation (HP-ACES) system for saline wastewater treatment is proposed. Nevertheless, it is discovered that the system evaporation efficiency (SEE) is low and solid crystalline salt does not precipitate during the experiment. Therefore, an established and validated mathematical model of the HP-ACES system is provided and the average relative errors of the system evaporation rate (SER) and SEE are 5.3 % and 7 %, respectively. The theoretical analysis of the non-optimized system indicates that the main cause of the low SEE of the HP-ACES system during the test is incorrect cooling water flow. Hence, the system optimization scheme is presented and the comparison of the HP-ACES system's performances before and after optimization is conducted. Results demonstrate that the SEE of the optimized HP-ACES system has been greatly improved (optimized system: 0.62–2.46 kg/kWh, non-optimized system: 0.77–0.91 kg/kWh). Additionally, there is almost no change in the yield and composition of the solid crystalline salt when the inlet air temperature of spray separation tower is above 90 °C.

There is a gradual increase in the discharge of saline wastewater which is harmful to the environment and human health. A novel heat pump air circulation evaporating separation (HP-ACES) system for saline wastewater treatment is proposed and experimentally investigated. The results show that the system evaporation efficiency (SEE) initially decreases before increasing with the expansion valve opening at different refrigerant charges. However, the SER increases by 0.67 kg/h at 1.21 kg refrigerant charge while they descend and then rise at 1.53 and 1.86 kg refrigerant charges. Noted that the effects of expansion valve opening on the SER and SEE are lessened with refrigerant charge. Moreover, there exist the optimal values of total air flow and air flow proportion of packing concentration tower at which the SER and SEE peak, and both of the solution concentration and cooling water flow negatively affect the SER and SEE. Besides, compared with similar evaporating separation systems, the HP-ACES system has higher energy efficiency for treating high saline wastewater and the SEE ranges from 0.86 to 1.64 kg/kWh (5–25 % CaCl2 solution). However, the SEE is low, and it is necessary to reveal the cause and propose the optimization schemes in the future.

In this study, we introduce a two-dimensional (2D) human skeletal model coupled with knee, spine, and shoulder exoskeletons. The primary purpose of this model is to predict the optimal lifting motion and provide torque support from the exoskeleton through the utilization of inverse dynamics optimization. The kinematics and dynamics of the human model are expressed using the Denavit–Hartenberg (DH) representation. The lifting optimization formulation integrates the electromechanical dynamics of the DC motors in the exoskeletons of the knee, spine, and shoulder. The design variables for this study include human joint angle profiles and exoskeleton motor current profiles. The optimization objective is to minimize the squared normalized human joint torques, subject to physical and task-specific lifting constraints. We solve this optimization problem using the gradient-based optimizer SNOPT. Our results include a comparison of predicted human joint angle profiles, joint torque profiles, and ground reaction force (GRF) profiles between lifting tasks with and without exoskeleton assistance. We also explore various combinations of exoskeletons for the knee, spine, and shoulder. By resolving the lifting optimization problems, we designed the optimal torques for the exoskeletons located at the knee, spine, and shoulder. It was found that the support from the exoskeletons substantially lowers the torque levels in human joints. Additionally, we conducted experiments only on the knee exoskeleton. Experimental data indicated that using the knee exoskeleton decreases the muscle activation peaks by 35.00%, 10.03%, 22.12%, 30.14%, 16.77%, and 25.71% for muscles of the erector spinae, latissimus dorsi, vastus medialis, vastus lateralis, rectus femoris, and biceps femoris, respectively.

Box delivery is a complicated manual material handling task which needs to consider the box weight, delivering speed, stability, and location. This paper presents a subtask-based inverse dynamic optimization formulation for determining the two-dimensional (2D) symmetric optimal box delivery motion. For the subtask-based formulation, the delivery task is divided into five subtasks: lifting, the first transition step, carrying, the second transition step, and unloading. To render a complete delivering task, each subtask is formulated as a separate optimization problem with appropriate boundary conditions. For carrying and lifting subtasks, the cost function is the sum of joint torque squared. In contrast, for transition subtasks, the cost function is the combination of joint discomfort and joint torque squared. Joint angle profiles are validated through experimental results using Pearson’s correlation coefficient (r) and root-mean-square-error (RMSE). Results show that the subtask-based approach is computationally efficient for complex box delivery motion simulation. This research outcome provides a practical guidance to prevent injury risks in joint torque space for workers who deliver heavy objects in their daily jobs.

A method to identify optimal strain sensor placement for examining structural static responses is presented. The method is based on the use of numerical optimization. Based on an assumed set of applied static forces, the optimal sensor placement can be obtained, and the measured strains can be used to provide the information needed to describe the structural stiffness. For example, the cross-sectional area can be determined by minimizing the difference between the analytical and measured strains. This approach is used to identify the optimized sensor placement. The objective of this study is to identify the minimum number of static strain sensors and the optimal sensor layout needed to evaluate a bridge’s structural condition. This study includes an automatic model parameter identification method, optimal static strain sensor placement, damage detection, and application to an actual bridge.