Identifying the parameters of solar photovoltaic (PV) cell models accurately and reliably is crucial for simulating, evaluating, and controlling PV systems. For this reason, we present an improved chimp optimization algorithm (IChOA) for the generation of precise and reliable solar PV cell models. As a new and improved version of the standard chimp optimization algorithm (ChOA), IChOA embeds two mutation rules in ChOA that include the elite opposition-based learning and visual search mechanism. The first rule is applied to strengthen global exploration capacity of ChOA, and the second one is utilized to enhance ChOA’s local exploitation ability (convergence accuracy). Based on the six benchmark test functions with different characteristics, the effectiveness of IChOA is evaluated by comparing to other five well-known optimization algorithms. The results suggest that IChOA offers superior performance over other competing algorithms. Finally, IChOA’s performance is confirmed through optimizing parameters for three widely employed mathematical models, specifically the single diode model, the double diode model, and the multi-cell PV modules. The findings prove the excellent performance of the suggested approach.

Dissimilar metal welds (DMWs), widely used in many components of nuclear power plants, may cause catastrophic failures because of damages (such as corrosion). Pulsed eddy current testing (PECT) could be used for DMW detection for its advantages of broadband property and high lift-off immunity. However, the research on PECT for DMW detection has seldom been studied because its structure and material are complicated. In this paper, the detection and evaluation of flaws in DMWs are studied based on the transmitter-receiver (Tx-Rx) PECT probe. Simulation and experiment studies show that, for better detection accuracy, the Tx coil of the probe should be located above the pressure vessel, the Rx coil should be located above the weld, and the minimum distance between the Tx and Rx coils is suggested. Moreover, the peak value of the differential signal and the coefficient a₁ of the fitted differential signals with a second-order Gaussian function can be used for defect evaluation. The research in this paper is beneficial for DMW detection by using PECT Tx-Rx probes.

The cement clinker compressive strength (CCCS) at the specified age is a significant indicator of the clinker quality. However, as for the existing CCCS measurement process, the test block must undergo standard curing for a specified age (long waiting time), which is of poor timeliness. Cement production enterprises have long yearned for a new approach to quickly obtain the CCCS values. In this article, a new method for predicting the CCCS with high accuracy immediately using only process information that can be quickly detected is proposed. First, a more efficient feature selection technique is designed to screen out the key input variable group for the CCCS modeling. Then, considering that the traditional single kernel support vector regression (SVR) is difficult to have both good learning and generalization abilities at the same time, the advantages of polynomial kernel function and K-type kernel function are combined to construct hybrid kernel SVR, which serves as the model structures of the 3 days’ CCCS (R3) and 28 days’ CCCS (R28). Finally, an improved grey wolf optimizer algorithm is proposed for the global optimal estimation of CCCS model parameters to further improve the prediction accuracy of the constructed models. The effectiveness of the overall work is verified through various comparative experiments. The experimental results prove that our proposed method can quickly establish accurate estimation models. The optimized SVR can predict the CCCS at specified ages very accurately and timely, which is beneficial for the production of various types of cement in industry.

Aluminum alloys under long-term service or repetitive stress are prone to small fatigue cracks (FCs) with arbitrary orientations, necessitating eddy current probes with focused magnetic fields and directional selectivity for reliable detection. This study presents a flexible printed circuit board (FPCB) probe with a double-layer planar excitation coil and a double-layer differential receiving coil. The excitation coil employs a reverse-wound design to enhance magnetic field directionality and focusing, while the differential receiving coil improves sensitivity and suppresses common-mode noise. The probe is optimized by adjusting the excitation coil overlap and the excitation–receiving coil angles to maximize eddy current concentration and detection signals. Finite element simulations and experiments confirm the system’s effectiveness in detecting surface cracks of varying sizes and orientations. To further characterize these defects, two time-domain features are extracted: the peak-to-peak value ($\Delta$P), reflecting amplitude variations associated with defect size and orientation, and the signal width ($\Delta$W), primarily correlated with defect angle. However, substantial overlap in their value ranges for defects with different parameters means that these features alone cannot identify which specific parameter has changed, making prior defect classification using a Transformer-based approach necessary for accurate quantitative analysis. The proposed method demonstrates reliable performance and clear interpretability for defect evaluation in aluminum components.

The lift-off effect constitutes a significant challenge in pulsed eddy current testing (PECT) for pipelines with insulation. Although the lift-off intersection (LOI) is regarded as an effective feature due to its insensitivity to lift-off, it is typically observable only in non-ferromagnetic materials, and absent in ferromagnetic ones. To overcome this constraint, a Derivative-to-Integral Ratio LOI (DIR-LOI) method, which is available for both ferromagnetic and non-ferromagnetic materials is proposed. Firstly, an analytical model of pipelines with insulations is built, serving as the theoretical foundation. Secondly, the DIR-LOI feature extraction method is developed based on the model, whereby the DIR-LOI is calculated as the ratio of the derivative to the integral of the differential PECT signal. Then, both simulation and experimental results indicate that the proposed DIR-LOI feature is applicable to both ferromagnetic and non-ferromagnetic materials. In addition, for non-ferromagnetic materials, the DIR-LOI time achieved a relative error of 1.3% for the thickness measurement, lower than that of the conventional LOI feature of 10.7%. For ferromagnetic materials, its relative error for the thickness measurement is 0.25%, lower than that of the differential signal peak time of 10.5%. These results demonstrate that the DIR-LOI feature exhibits higher measurement accuracy than conventional features. This study is beneficial for eliminating the lift-off effect in PECT for both ferromagnetic and non-ferromagnetic materials and will be helpful for corrosion detection of pipelines such as jacketed tubular reactors in chemical plants, insulated pressure vessels in nuclear power facilities, and so on.

Model Plant Mismatch (MPM) significantly impacts the control performance of MPC systems. Despite many research focus on assessing channel quality, more root location of MPM is expected. To thoroughly diagnose the channel and model parameter mismatch for MIMO-MPC following up on the previous investigation into MPM sub-model location method, a drill-down root cause MPM diagnosis method via high-dimensional features based support vector machine classifier is proposed in this paper. Firstly, the dynamic relationship between model parameters and system performance is derived in the time domain, which is the theoretical basis for the diagnosis method. Furthermore, for the purpose of extract the operational characteristics of the system, a high-dimensional feature extraction method from running data is proposed, which extracts channel features and parameter features separately during the stabilization phase and regulation phase. Finally, to diagnose the root cause of MPM, a multi-level support vector machine classifier diagnosis method based on Bayesian optimization and mixed kernel function is presented. The validity of the proposed diagnostic method is confirmed through experiments on Wood-Berry distillation column simulation platform and an industrial process - air separator .