Considering that compressive strength (CS) is an important mechanical property parameter in many design codes, in order to ensure structural safety, concrete CS needs to be tested before application. However, conducting CS tests with multiple influencing variables is costly and time-consuming. To address this issue, a machine learning-based modeling framework is put forward in this work to evaluate the concrete CS under complex conditions. The influential factors of this process are systematically categorized into five aspects: man, machine, material, method and environment (4M1E). A genetic algorithm (GA) was applied to identify the most important influential factors for CS modeling, after which, random forest (RF) was adopted as the modeling algorithm to predict the CS from the selected influential factors. The effectiveness of the proposed model was tested on a case study, and the high Pearson correlation coefficient (0.9821) and the low mean absolute percentage error and delta (0.0394 and 0.395, respectively) indicate that the proposed model can deliver accurate and reliable results.

To improve integration and achieve better coal industry materials and energy balance, integrated collaborative supply chains (SCs) are needed. However, as single-core SC models are not suitable for complex coal industry systems, a multicore, correlated, conditional SC model, called a supply chain network (SCN), is proposed. SCN collaborative evaluation models are discussed including industrial metabolic balance (IMB), enterprise profitability, contract execution ability and information interaction ability, for which IMB is used as the efficiency index of resource coordination of SCN, also as the constraints of the models on system levels. Further, data modeling by using BP-ANN algorithm is used to predict the profitability of supply chain network. Finally, the feasibility of the above models is illustrated by cases. The proposed evaluation models in this paper form the scientific and quantitative evaluation method of SC, which could be used for both SC planning and operations management helping detect and eliminate risks.

The stable quality of circulating water ensures the long-term stable operation of various processes in petrochemical production and achieves energy savings and emission reduction while reducing environmental pollution and yielding economic benefits to petrochemical enterprises. However, traditional circulating water quality evaluation and modeling for corrosion rate prediction suffer from adaptability and accuracy problems. To address these problems, the water quality analysis data of the circulating water in the field were subjected to data preprocessing and water quality index calculation to perform feature engineering, followed by modeling using a machine learning method that integrates the adaptive immune genetic algorithm and random forest (RF) algorithm and can intelligently select the water quality parameters to be used as the input variables for the RF modeling. Finally, the method was validated using an industrial example, and the results indicate that the method is capable of removing interference variables and is suitable for carbon steel corrosion rate prediction based on water quality models. The proposed method provides a basis for water quality management and real-time decision-making by circulating water field personnel.