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Immobilized amines adsorbents have excellent CO2 adsorption capacity and low regeneration energy consumption, which have broad application prospect, but poor reusability. To improve the cyclic adsorption performance of immobolized amines for CO2 capture, the mesoporous silica A‐MSU‐J with carboxyl groups on the surface was synthesized, and immobilized amine A‐MSU‐J‐TEPA was prepared by introducing tetraethylenepentamine (TEPA) by impregnation method. The introduction of carboxyl groups onto the carrier surface can form stronger hydrogen bonds with TEPA than the initial hydroxyl groups, thus achieving stable loading of TEPA. When the TEPA load was 50 wt %, the adsorption capacity of 4A‐65MSU‐J‐50TEPA declined by only 13.7 % (from 2.58 to 2.23 mmol ⋅ g−1, at 70 °C, 0.1 Mpa) after 10 cycles, while the adsorption capacity of 65MSU‐J‐50TEPA decreased by 23.6 % (from 2 to 1.53 mmol ⋅ g−1). Furthermore, carrier pore size also affects the adsorption capacity of immobilized amines for CO2. The adsorption capacity of 4A‐65MSU‐J‐50TEPA with larger pore size (2.95 mmol ⋅ g−1, at 55 °C) is much higher than that of 4A‐25MSU‐J‐50TEPA with smaller pore size (2.17 mmol ⋅ g−1). In conclusion, improving the interaction between organic amines and the carrier surface and using carriers with larger pore size can enhance the adsorption capacity and reusability of immobolized amines adsorbents. [ABSTRACT FROM AUTHOR]

In this article, the problem of decentralized full-state constrained adaptive tracking control is investigated for interconnected time delay systems with input saturation. The extreme learning machine is applied to approximate the unknown nonlinear function in the design process. By selecting the appropriate Lyapunov–Krasovskii function, the time delay interconnection term could be compensated. A unified barrier function is introduced to ensure that the constraints imposed on all states are not violated. The Nussbaum function is introduced to deal with the problem of input saturation. By applying adaptive backstepping procedures, a novel decentralized full-state constrained adaptive tracking control strategy is proposed for the considered interconnected time delay systems. In terms of Lyapunov stability theory, it is proved that all the signals of the closed-loop systems are semi-global ultimately uniformly bounded and all state variables could be constrained within the time varying asymmetric boundaries. Finally, the effectiveness of the proposed scheme is illustrated by a simulation example of a two-stage chemical reactor. [ABSTRACT FROM AUTHOR]

In this study, a prescribed performance adaptive fault tolerant tracking control scheme is presented for a class of nonlinear large‐scale systems with time delay interconnection, dead zone input, and actuator fault. The radial basis function neural networks are used to approximate unknown nonlinear functions. Different from the barrier Lyapunov functions used to achieve the symmetrical prescribed performance, a new error transformation is introduced in this study to achieve the desired asymmetrical prescribed performance. In addition, Nussbaum function is introduced to solve the difficulties caused by dead zone input and actuator fault. Based on the appropriate Lyapunov–Krasovskii functions, the effect of time delay interconnection could be compensated. By using backstepping procedures, an adaptive fault tolerant tracking control approach is developed for the considered large‐scale systems, and the stability of the closed‐loop systems is analyzed by Lyapunov theory. Meanwhile, the prescribed performance of the tracking error could be guaranteed. Finally, the effectiveness of the proposed control approach is illustrated by two simulation examples. [ABSTRACT FROM AUTHOR]

In order to effectively reduce the energy consumption of high sulfur natural gas purification process, in this paper, optimization model based on the genetic algorithm (GA) was developed. The natural gas purification process steady state model was established by using process simulation software ProMax. 8 key operating parameters of the purification system were determined by the process simulation model and energy consumption analysis. To reduce the calculation time and to solve the no convergence problems in the process simulation model, the BP (Back Propagation) neural network model was applied to train and test the simulated data. Then the BP model was incorporated into Genetic Algorithms to develop the energy consumption optimization model. A case study was performed in a high-sulfur natural gas purification plant with the capacity of 300×10 4 Nm 3 /d. And the results demonstrate that the energy consumption of the purification plant was reduced by 12.7%. [ABSTRACT FROM AUTHOR]