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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.
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[This corrects the article DOI: 10.1039/D0RA07257E.].
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Mooney viscosity is an essential parameter in quality control during the production of nitrile-butadiene rubber (NBR) by emulsion polymerization. A process model that could help understand the influence of feed compositions on the Mooney viscosity of NBR products is of vital importance for its intelligent manufacture. In this work, a process model comprised of a mechanistic model based on emulsion polymerization kinetics and a data-driven model derived from genetic programming (GP) for Mooney viscosity is developed to correlate the feed compositions (including impurities) and process conditions to Mooney viscosity of NBR products. The feed compositions are inputs of the mechanistic model to generate the number-, weight-averaged molecular weights (M n, M w) and branching degree (BRD) of NBR polymers. With these generated data, the GP model is used to output the optimal correlation for the Mooney viscosity of NBR. In a pilot NBR production, Mooney viscosity data of NBR predicted by the process model agree quite well with experimental values. Furthermore, the process model enables the analyses of the univariate and multivariate influence of feed compositions on NBR Mooney viscosity, and the variables include the contents of vinyl acetylene and dimer in 1,3-butadiene, as well as the mass flow rate of the chain transfer agent (CTA) in the process. Based on the results, it is recommended to control the content of vinyl acetylene in the 1,3-butadiene feed below 14 ppm and the content of dimer below 1100 ppm. This developed process model would help stabilize NBR viscosity for a better control of the product quality.
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This paper presents a comprehensive study on removal of chromium(III) from aqueous waste solution using emulsion liquid membrane (ELM). The study has highlighted the importance of emulsion stability for maximizing the removal of chromium(III). The ELM consists of tri-n-butyl phosphate (TBP) as a carrier, commercial kerosene as organic solvent, sulfonated liquid polybutadiene (LYF) as surfactant agent, sulfuric acid, deionized water or sodium hydroxide as stripping phase. The important factors studied which affected the ELM stability and removal of chromium(III) were the concentrations of surfactant (2-8% w/w), carrier (2-10% w/w), internal phase H(2)SO(4) [pH 0-6], deionized water [pH 6.65] and NaOH (0-0.8% w/w), transfer time (5-35 min) and the effect of volume ratio of the feed solution to the emulsion phase (Rf) (5:1-9:1). At the optimum condition it was possible to remove 99.71-99.83% of chromium(III) by using ELM. LYF was not only the surfactant but also played a key auxiliary effect for TBP combining with chromium(III) by studying on the transport mechanism.
