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The rising industrial sector capacity increases carbon emissions, necessitating a low-carbon transformation for global sustainability. Accurate multi-step forecasting of industrial carbon emissions is crucial for optimizing production, reducing energy consumption, improving efficiency, and achieving decarbonization. However, current forecasting faces challenges like model uncertainty and low accuracy with multi forecasting steps. To address these issues, this study proposed a multi-step carbon emissions forecasting model for industrial processes. Firstly, a new multi-step forecasting strategy framework was proposed. Secondly, SHapley Additive exPlanations (SHAP) and lagged autocorrelation analysis were used to extract key features affecting industrial carbon emissions. On this basis, the grey model, autoregressive moving average (ARMA) model, and least squares support vector machine (LSSVM) method were used to build forecasting models separately. The weights of the three models were solved using the induced ordered weighted average operator and the Monte Carlo search tree (MCST) method. The final forecasting results were obtained by using the weighted summation method. The data collected from the real industry were used to validate the proposed model. The results showed that the proposed model could accurately forecast industrial carbon emissions within 12 steps and its MAPE is less than 10%. The proposed multi-step industrial carbon emissions forecasting model can provide data support for industrial energy saving, carbon reduction, and green and sustainable development.

We reveal the origins of surface groups on pristine and defective graphene after oxidation with potassium permanganate in aqueous solution. Density functional theory calculations show that the hydroxyl group that is first introduced onto graphene via hydrolysis of permanganate ester plays an important role in the oxidative cutting of graphene. Our results demonstrate that oxidative unzipping of defect-free graphene will occur from the edge and the inner plane simultaneously in terms of comparable maximum barrier heights along these two reaction routes. Divacancy on the defective graphene may accelerate the etching process when the hydroxyl group is introduced at the defect edge. Different from the previous unzipping mechanisms producing graphene flakes with only zigzag edges, our new mechanism involves hydroxyl and both sides of the graphene sheet, which allows richer edge states after oxidative cutting that agree with experimental observations. The detailed molecular insight into the mechanisms for graphene oxidation and fragmentation will be valuable for developing an effective means for graphene manipulation and interpretation of the long-puzzling graphene oxide structure.

Amid a surge in waste volume, the need to achieve sustainable waste treatment has become increasingly important. Here, we present a protocol for the design and accelerated optimization of a waste-to-energy system using artificial intelligence tools. We describe steps for waste treatment process advancement as demonstrated by the medical waste-to-methanol conversion and implementing data-driven process optimization. We then detail procedures for streamlining tasks by establishing connectivity between systems such as Aspen Plus and MATLAB.