The primary aim of this study is to understand the effect of metal oxide flux on the fusibility of high-calcium coal ash. Based on the decomposition rate, the evolution of mineral matters in high-calcium coal has been investigated. The ash fusion temperatures of samples are measured by adding different flux Al₂O₃, Na₂O, K₂O, MgO, and TiO₂. The results show that Na₂O is the most effective in lowering ash fusion temperatures and its flow temperature could be 110 °C lower than that of the original ash. FactSage is used to calculate the proportion of solid phase and the mineral compositions as a function of the ash compositions and temperature. With the increase of Na₂O, mineral matters with a low melting point form in the mixture. Furthermore, the decomposition rate of mineral matters increases in the first stage. The phase diagrams and relative mineral variation illustrate that the mineral and the decomposition rate variations are the main reasons for the change of ash fusion temperatures.

Co-combustion is a crucial route for the high-efficiency utilization and clean conversion of different carbonaceous feedstocks (biomass, coal, petroleum coke, etc.). The migration and transformation of alkali and alkaline earth metals (AAEMs) are not only related to ash-related issues in actual application, but also directly affect the reaction behavior of binary particles during co-conversion. This review paper summarizes research progress in the detection methods (online and offline) and influencing factors (feedstock type, feedstock blending ratio, reaction temperature, reaction time) of AAEMs migration and transformation during the co-combustion of carbonaceous feedstocks. Furthermore, it provides a detailed summary of research progress on factors (feedstock blending ratio, heating rate, etc.) influencing the co-combustion reactivity of carbonaceous feedstocks, synergy behavior, and its mechanisms. The influence of feedstock type on AAEMs migration and transformation during co-combustion is mainly related to the composition categories, chemical forms and contents of intrinsic mineral in binary feedstocks. The increase in the combustion temperature will intensify the release of inherent AAEMs in carbonaceous feedstocks, and promote AAEM deactivation. For high K and Cl-containing biomass, a higher biomass proportion in blends would result in more AAEMs release during the co-combustion process. Conversely, an increase in coal proportion in blends will directly favor the reduction or inhibition of AAEMs release. Synergy behavior during co-pyrolysis and subsequent char co-combustion is usually presented as an inhibition effect and an synergistic effect, respectively. The synergistic mechanisms of carbonaceous feedstock co-combustion reactions can be divided into two categories: non-catalytic synergistic mechanisms related to the excitation and migration of biomass-based free radicals and catalytic synergistic mechanisms related to biomass-based AAEMs catalysis. Additionally, future research prospects are also proposed based on the systematic review.

In this paper, an improved heat exchanger network (HEN) synthesis method based on the comprehensive learning particle swarm optimizer algorithm (CLPSO) is proposed to synthesize HENs without stream splits. Compared with the standard particle swarm algorithm, CLPSO employs a novel learning strategy that preserves the diversity of the swarm to discourage premature convergence. However, while the algorithm’s global exploration capability is enhanced, the local search capability decreases and the convergence speed becomes slow. In addition, the solution quality of CLPSO is largely determined by the randomly generated particles’ best previous position (pbest) during initialization. Hence, the solution may be unstable due to different pbest. For the abovementioned considerations, this paper proposes a new HEN initialization and renovation method to improve the quality of pbest, reduce the initial cost, and retain the obtained optimization results as much as possible in the optimization process to speed up the convergence of the algorithm. Four typical cases are simulated to verify the effectiveness of the proposed method. This method only needs a single-level optimization algorithm to obtain high-quality solutions, which will give it a bright prospect in research and application.