To know the sustainable performance of calcium-based adsorbents is one of the important aspects to realize efficient and economical carbon capture, and to systematically study the properties of natural adsorbents is conducive to their industrialization. The cyclic calcination and carbonation characteristics of a typical natural limestone were investigated using a thermal gravimetric analyzer. Two kinds of over-sintering conditions were selected to emphatically study the cyclic separation of CO₂ from limestones through prolonging the calcination time and increasing the calcination temperature. The results showed that the untimely end of the chemical reaction control stage caused by excessive sintering is the direct reason for the reduction in cyclic carbonation conversion, and the changes in surface morphology of calcined products due to pore collapse and fusion are the fundamental reasons for the reduction in cyclic carbonation conversion. The excessive sintering caused by extending the calcining time or increasing the calcining temperature has great inhibition on this cycle only; the inhibition decreases rapidly in subsequent cycles. In addition, SEM and BET–BJH tests further confirm the influence of the over-sintering phenomenon. With the further increase in cycle number, the early excessive sintering has certain stimulative effects on the subsequent carbonation reaction. It is expected to provide a reference for the subsequent research and development of natural calcium-based adsorbents.

The absorption heat exchanger with a large temperature difference has a higher heat transfer superiority than the other heat exchangers (including plate heat exchanger), which is more suitable for long-distance heating. To improve its system performance, parameter collaborative optimization (including building accurate predictive models) has become an effective method because it does not require too much investment. In this study, a heat exchange station was chosen as a case study, and a model of a long short-term memory (LSTM) neural network was used to predict the temperatures of primary return water and secondary return water. Accordingly, the reliability of the fitting result based on the model was confirmed through a contrastive analysis with the prediction results of a support vector machine (SVM) model, a random forest (RF) model, and an extreme gradient boosting (XGBoost) model. In addition, the algorithm of particle swarm optimization was used to optimize the flow rate of primary supply water. The results showed that the temperature of primary-side return water decreased from 29.6 °C to 28.2 °C, the temperature of secondary-side return water decreased from 39.8 °C to 38.6 °C, and the flow rate of primary-side supply water decreased from 39 t/h to 35.2 t/h after the optimization of the flow rate of primary supply water. The sensibility assessment emerged that the secondary-side flow rate to the secondary-side supply water temperature was about 7 times more sensitive than the primary-side supply water temperature, and concretely, the lower the temperature, the higher the sensibility. In summary, the accuracy of the proposed prediction model was validated and the optimization direction was pointed out, which can be used to provide guidance for designing and planning absorption heat exchange stations with large temperature differences.