B Wang

Abstract This paper makes innovative research on developing a data-driven control strategy under the Energy Internet of Things architecture. On the one hand, the platform aims to provide data representation and interpretable analysis for different stakeholders (end users, construction operators or managers) to realize the flexibility and scalability of the platform; on the other hand, it can improve the thermal comfort and also reduce the power consumption of buildings. However, to process vast amounts of data, it is critical to select appropriate control methods and design optimization issues. Data-driven predictive control (DPC) is a control technology that replaces model-based predictive control (MPC). When applied to complex building operations, MPC is implemented by using the control-oriented data-driven model. The key to DPC technology is the use of CatBoost algorithm, which is highly interpretable and easy to be operated by stakeholders. This paper chooses TDNN, LightGBM, and CatBoost to compare and analyze building energy consumption. Numerical simulation results show that the CatBoost algorithm’s performance is better than other algorithms, and the complexity and implementation cost is significantly reduced.

The concept and method of Life Cycle Assessment are Analyzed. A general product life cycle flow chart and product envirorunental efficiency evalUaion and improvement mathematcs model were established,which is explained with a case.

In this study, a polyvinyl alcohol/carboxymethyl chitosan (PVA/CCTS) hydrogel was synthesized via free radical polymerization and employed for the adsorption of Rhodamine B (RhB) from aqueous solution. The hydrogel was systematically characterized by FTIR, SEM, XPS, and BET analyses, confirming its interconnected porous network and functional group composition. Under optimized conditions (adsorbent dosage = 0.1 g, pH = 6, RhB concentration = 65 mg·L−1, and T = 298.15 ± 2 K), the maximum adsorption capacity reached 15.88 mg·g−1. Kinetic analysis showed that the pseudo-second-order model best described the adsorption behavior under optimal conditions, indicating that the uptake of RhB is governed by multiple interaction mechanisms rather than simple physisorption alone. The equilibrium data were best fitted by the Freundlich isotherm (R2 = 0.976), indicating surface heterogeneity of the hydrogel. Thermodynamic evaluation revealed an endothermic (ΔH = 28.38 ± 4.40 kJ·mol−1), with adsorption efficiency improving at elevated temperatures. The hydrogel retained appreciable adsorption capacity after three adsorption–desorption cycles (5.78 mg·g−1 at the third cycle). Density functional theory (DFT) calculations identified -COOH and -NH2 groups as the primary active sites, and molecular electrostatic potential analysis confirmed that electrostatic interactions between the negatively charged hydrogel surface and cationic RhB drive the initial adsorption. Molecular dynamics (MD) simulations over 100 ns further demonstrated that van der Waals forces constitute the dominant driving force, supplemented by electrostatic interactions and hydrogen bonding, with the hydrogel’s cross-linked network stabilizing adsorbed RhB molecules. The integrated experimental computational approach provides a comprehensive mechanistic understanding of RhB adsorption on PVA/CCTS hydrogel, offering guidance for the rational design of polysaccharide-based adsorbents for dye-contaminated wastewater treatment.