A simple nonlinear control strategy using sparse kernel learning (SKL) with a polynomial kernel form is presented and applied to chemical processes. The nonlinear process is first identified by SKL with a polynomial kernel, and then a predictive control performance index is formulated. This index is characterized as an even-degree polynomial function of the manipulated input and has the benefit that the input can be separated from the index because of its special structure. Consequently, the optimal manipulated input can be efficiently obtained by solving a simple root problem of an odd-degree polynomial equation. Moreover, the control parameter directly relates to its performance and can be tuned in a guided manner. All these attributes result in a practicable solution for real-time process control. The novel controller is applied to two chemical processes to evaluate its performance. The obtained results show the superiority of the proposed method compared to a well-tuned proportional−integral−derivative controller in different situations.

To analyze the impact of digital inclusive finance and human capital on inclusive green economic development in China, we build a comprehensive indicator system to measure the level of inclusive green development and use the super-efficiency SBM method to measure the inclusive green total factor productivity (IGTFP) in Chinese cities, then the system GMM model is used to empirically test the direct and interactive influences. Inclusive green development in China has maintained a growing trend in recent years, reaching a peak in 2017. The development of digital inclusive finance in terms of breadth, depth and degree of digitization is conducive to promoting inclusive green development. Although human capital does not directly affect inclusive green development, it plays a significantly positive moderating role in the process of digital inclusive finance promoting inclusive green development. In this paper, the impact of digital inclusive financial and human capital and their interactions on inclusive green development is analyzed within a unified framework, which has important practical significance for the orderly promotion of the development of digital inclusive finance, improving residents’ education level and promoting inclusive green development.

Nitrated polycyclic aromatic hydrocarbons (nPAHs) are ubiquitous environmental pollutants, which exhibits higher toxicity than their corresponding parent PAHs (pPAHs). Recent studies demonstrated that the nPAHs could represent major soil pollution, however the remediation of nPAHs has been rarely reported. In this study, biological, physical, and chemical methods have been applied to remove 1-nitropyrene, the model nPAH, in contaminated soil. A comparative study with pyrene has also been investigated and evaluated. The results suggest that the physical method with activated carbon is an efficient and economical approach, removing 88.1% and 78.0% of 1-nitropyrene and pyrene respectively, within one day. The zero-valent ion has a similar removal performance on 1-nitropyrene (83.1%), converting 1-nitropyrene to 1-aminopyrene in soil via chemical reduction and decreasing the mutagenicity and carcinogenicity of 1-nitropyrene. Biological remediation that employs scallion as a plant model can reduce 55.0% of 1-nitropyrene in soil (from 39.6 to 17.8 μg/kg), while 77.9% of pyrene can be removed by plant. This indicates that nPAHs might be more persistent than corresponding pPAHs in soil. It is anticipated that this study could draw public awareness of nitro-derivatives of pPAHs and provide remediation technologies of carcinogenic nPAHs in soil.

Rubber mixing process is a typical non-linear fed batch process without well developed mechanism. Soft-sensing modeling of the mixture's Mooney viscosity is crucial and challenging since this index is an important process criterion to judge the quality of rubber compounds while the measurement of Mooney viscosity is time-consuming and laborious to assay.Furthermore, the mixing process is drifting and volatile even noisy; only few data samples could be used to modeling. In present paper, an adaptive least contribution elimination kernel learning (ALCEKL) approach is proposed to predict the Mooney viscosity. It adopts a sparsity strategy of least contribution elimination and presents a buffer based learning algorithm associated with improved space angle index (SAl) strategy. Experiments on field data indicate that proposed approach is more robust and accurate than other kernelized modeling methods with feasible computational complexity under field circumstances.