In this study, In-situ precipitation reaction was used to prepare paraffin@SnO₂/CNTs composite phase change materials. The paraffin@SnO₂/CNTs composites material were prepared by using paraffin as a phase change core material, and tin dioxide and carbon nanotubes as composite shell materials. The microstructure, chemical composition and crystal phase structure of the composites were analyzed by Scanning electron microscope (SEM), Fourier infrared spectrum (FT-IR) and X-ray diffraction (XRD). The thermal performance and thermal stability of phase change materials were determined by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The microcapsule has a core–shell structure with a diameter of 2–5 μm. The encapsulation efficiency of the paraffin@SnO₂/CNTs composite was calculated to be 42.94%. The slurry is formed by dispersing microcapsules in water, which not only greatly improves the specific heat capacity, thermal conductivity, visible light absorption, but also makes the effective receiving efficiency reach 91.79% at 40 °C, indicating that it is a promising heat transfer working slurry in the field of Direct Absorption Solar Collectors (DASC) system.

Model-free adaptive controller (MFAC) is a novel data-driven control methodology that relies only on input/output (I/O) measurement data instead of classic mathematical models of actual controlled plants. The single-input single-output (SISO) compact-form MFAC (SISO-CFMFAC) is a promising method for controlling SISO nonlinear time-varying systems. The parameters in SISO-CFMFAC must be carefully tuned before use, as inappropriate parameters may lead to poor control performance. However, up to now, parameter tuning has been a time-consuming and laborious task. In this paper, a new approach called SISO-CFMFAC-LSTM is proposed for parameter self-tuning of SISO-CFMFAC based on long short-term memory (LSTM) neural network. To evaluate the performance of the proposed methodology, qualitative and quantitative comparisons with other existing control algorithms are carried out. Six individual performance indices, namely, the root mean square error (RMSE), the integral absolute error (IAE), the integral time-weighted absolute error (ITAE), the integral absolute variation of the control signal (IAVU), the maximum overshoot (MO), and the imprecise control ratio (ICR), are introduced for quantitative comparison. The experimental results demonstrate that the proposed SISO-CFMFAC-LSTM achieves the best performance in all indices, indicating that it is an effective control method for SISO nonlinear time-varying systems.

This article investigates the partial-form model-free adaptive control (MFAC) issue for a class of discrete-time nonlinear systems. An improved partial-form MFAC design named IPFMFAC-NN is proposed, where neural networks are introduced to enhance the control performance. With the excellent approximation ability of radial basis function (RBF) neural networks, the pseudo gradient (PG) values of control method can be directly approximated online using the measured input and output data of the controlled system. Besides, long short-term memory (LSTM) neural networks are used to tune the essential parameters of the control method online with system error set and gradient information set. Finally, the effectiveness and applicability are verified by SISO discrete nonlinear system simulation and three-tank system simulation, and experimental results demonstrate that the proposed method achieves the best control performance in all five indices. Especially compared with the partial-form MFAC, the proposed method reduces the RMSE index by 43.83% and 6.39%, respectively in two simulations, making it a promising control method for discrete-time nonlinear systems.

An integrated predictive iterative learning control (IPILC) scheme for batch process is designed from a two-dimensional (2D) system point of view. The integrated control framework combines batch-wise ILC and time-wise model predictive control (MPC), referred as 2D-IPILC. In the trajectory tracking problem of batch process, the predictive model can be obtained based on the system response using 2D theory. The control profile in the current batch is updated by MPC, using a quadratic objective function defined over time horizon. The major advantages of the proposed design scheme are shown in the better tracking performance as well as faster convergence speed by taking into account the time-wise feedback control with-in the current batch. The simulation results demonstrate the effectiveness of the proposed scheme.