A heterojunction-redox catalysis strategy is proposed for fabricating a dual-functional catalyst/adsorbent to realize integration of high-temperature CO2 capture and in situ conversion.

Multi-objective optimization has received increasing attention over the past few decades，and a large number of nature-inspired metaheuristic algorithms have been developed to solve multi-objective problems. An external archive is often used to store elite solutions in multi-objective algorithms. Since the archive size is limited, it must be truncated when the number of nondominated solutions exceeds its maximum size. Thus, the archive updating strategy is crucial due to its influence in the performance of the algorithm. However, achieving a fast convergence speed while assuring diversity of the obtained solutions is always a challenging task. In this paper, a novel multi-objective particle swarm optimization algorithm based on a new archive updating mechanism which depends on the nearest neighbor approach, called MOPSONN, is proposed. Two archive updating strategies are adopted to update nondominated solutions in the archive, which are beneficial to accelerate the convergence speed and maintain diversity of the swarm. The performance of MOPOSNN is evaluated on several benchmark problems and compared with seven state-ofthe-art multi-objective algorithms, including four multi-objective particle swarm optimization algorithms and three multi-objective evolutionary algorithms. The experimental results demonstrate the significant effectiveness of MOPSONN in terms of convergence speed and spread of solutions.

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Dynamic optimization problems (DOPs) are widely encountered in complex chemical engineering processes. However, due to the existence of highly constrained, nonlinear, and nonsmooth environment in chemical processes, which usually causes nonconvexity, multimodality and discontinuity, handling DOPs is not a straightforward task. Heat transfer search (HTS) algorithm is a relative novel metaheuristic approach inspired by the natural law of thermodynamics and heat transfer. In order to solve DOPs efficiently, a new variant of HTS algorithm named quadratic interpolation based simultaneous heat transfer search (QISHTS) algorithm is proposed in this paper. The QISHTS algorithm introduces three modifications into the original HTS algorithm, namely the effect of simultaneous heat transfer search, quadratic interpolation method, and population regeneration mechanism. These three modifications are employed to provide lower computational complexity, as well as to enhance the exploration and exploitation capabilities. Therefore, the ensemble of these modifications can provide a more efficient optimization algorithm with well-balanced exploration and exploitation capabilities. The proposed variant is firstly investigated by well-defined benchmark problems and then applied to solve four chemical DOPs. Moreover, it is compared with different well-established methods existing in the literature. The results demonstrate that QISHTS algorithm has the greatest robustness and precision than other competitors.