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In order to address the issues of system fluctuation and low efficiency brought on by various disturbance factors in wastewater treatment plants (WWTPs), this study builds a robust multi-objective optimization model and determines the ideal parameters for enhancing the robustness and stability of energy consumption (EC) and effluent quality (EQ). Firstly, a novel robust index is proposed, merging the signal-to-noise ratio and standard deviation., comprehensively considering data volatility and anti-interference capability to guide the establishment of a robust optimization model. Secondly, considering the energy consumption errors and water quality data measurement caused by noise factors, which increases the difficulty and time complexity of the solution. Therefore, this paper uses a multi-objective mantis search algorithm (MOMSA) based on a robust subgroup, and an information synergy strategy is used to solve the problem. The strategy first divides the population into strong and weak robust subgroups according to their robustness. Then, the co-evolutionary strategy and the information enhancement strategy will be used to guide the evolutionary direction of the individual from the whole and the local perspectives, thereby comprehensively improving the convergence speed of the algorithm. Based on the test function, this shows that the convergence, diversity, and comprehensive performance of the improved algorithm are better than other algorithms. Experimental results based on the BSM1 platform show that the improved algorithm is effective in finding robust solutions with strong stability despite changes in external factors (e.g., precipitation). According to the experimental results based on real industrial problems, the new robustness metrics can effectively evaluate the robustness of the optimization model, and compared with other algorithms, the algorithm proposed in this paper yields solutions of more reliable quality and faster convergence performance, which can successfully solve the robust optimization problem in wastewater treatment plants. [ABSTRACT FROM AUTHOR]

Recently, solar‐driven interfacial evaporation (SDIE) has been developed quickly for low‐cost and sustainable seawater desalination, but addressing the conflict between a high evaporation rate and salt rejection during SDIE is still challenging. Here, a spatial confinement strategy is proposed to prepare the gel composite solar evaporator (SCE) by loading one thin hydrogel layer onto the skeleton of a carbon aerogel. The SCE retains the hierarchically porous structure of carbon aerogels with an optimized water supply induced by dual‐driven forces (capillary effects and osmotic pressure) and takes advantage of both aerogels and hydrogels, which can gain energy from air and reduce water enthalpy. The SCE has a high evaporation rate (up to 4.23 kg m−2 h−1 under one sun) and excellent salt rejection performance and can maintain structural integrity after long‐term evaporation even at high salinities. The SDIE behavior, including heat distribution, water transport, and salt ion distribution, is investigated by combining theoretical simulations and experimental results. This work provides new inspiration and a theoretical basis for the development of high‐performance interfacial evaporators. [ABSTRACT FROM AUTHOR]

The mechanical damage of corn kernels during harvest leads to mildew in the kernel storage process, seriously affecting food safety and quality. Impact force is the primary source of mechanical damage in the corn threshing process, and its accurate detection is of great significance for corn threshing with low damage. A method for the impact force detection of corn ears was proposed in this manuscript. The momentum theorem determined the main factors influencing impact force (weight, falling height, and space attitude). Corn ear weight, falling height, and space attitude were used as experimental factors. The bench test was carried out with the impact force of corn ear as the output variable. During the experiment, piezoelectric sensors were used to collect the impact force of corn ears under different motion states. Then, the impact force detection model was constructed using four machine learning algorithms: multiple linear regression, ridge regression, random forest, and support vector regression. The results showed that the RF algorithm was more suitable for constructing the prediction model of average and maximum impact force when corn ears fall, SD, RMSE, and r were, respectively: 0.9526, 1.2685, 0.9855; 3.8389, 3.6071, and 0.8510. Secondly, the weight characteristics had the most significant influence on the impact force detection of the ear. Therefore, this method can be used as an accurate, objective, and efficient online detection method for impact force. [ABSTRACT FROM AUTHOR]

Plasmonic materials have been extensively explored for surface-enhanced Raman scattering (SERS) due to their high tunability and excellent localized electric field enhancement. Most research for now has focused on noble metals, with limited investigation into corrosion-resistant materials for SERS effects. In this study, a photolithography process is firstly used to create a patterned dot array on a silicon substrate. Next, magnetron sputtering is employed to deposit molybdenum films, finally resulting in the molybdenum truncated cone array substrates for SERS applications. The fabricated truncated cone array experimentally facilitates the coupling of localized surface plasmon polaritons, consistent with simulation results obtained via the finite-difference time-domain method. The formation of hot spots between the cone unit cell arrays leads to the improved Raman signals and can act as traps for target molecules. This study demonstrates that molybdenum-based micro-nano structures can serve as reliable SERS substrates for sensitive molecular sensing applications in highly corrosive environments. [ABSTRACT FROM AUTHOR]