Yang Jiaqian

Bio: Yang Jiaqian is an academic researcher from Zhejiang University of Technology. The author has contributed to research in topics: Environmental pollution & Calcination. The author has an hindex of 1, co-authored 2 publications receiving 51 citations.

Neuro-genetic machine learning framework accelerates the optimization of Ag/MnOx catalyst for total oxidation of toluene

Abstract: A neuro-genetic machine learning framework (ANN-GA) was employed to optimize and predict the optimum preparation parameters for the precipitation synthesis of high-efficiency silver-doped manganese oxides (Ag/MnOx) for toluene total oxidation The preparation conditions of Ag/MnOx for maximum CO2 yield were predicted to be at 132 wt% of silver loading, 415 min of stirring time, 461 ℃ of calcination temperature, and 43 h of calcination time The resulting Ag/MnOx-GA catalyst achieved the lowest T50 (CO2) of 206 ℃ comparing to the catalyst suggested by the response surface methodology (RSM) (214 ℃) and synthesized catalyst Ag/MnOx-R16 (220 ℃) The obtained relative importance of each factor involved in the preparation process revealed that all of the parameters should be taken into account, among which the silver loading had the most significant impact on the catalytic reactivity The synthesized materials were characterized by ICP-OES, SEM/EDS, BET, Raman, XRD, XPS, TGA, H2-TPR, and O2-TPD The results indicated that the Ag/MnOx-GA exhibited the largest surface area (844), the lowest average oxidation state (AOS) of Mn (325), the best redox capacity, and the highest ratios of Oads/Olatt (075) These consequences provided evidence that the toluene total oxidation was more likely to occur on the surface of Ag/MnOx-GA at a relatively lower temperature

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

Abstract: Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Digital Transformation and Environmental Sustainability: A Review and Research Agenda

Abstract: Digital transformation refers to the unprecedented disruptions in society, industry, and organizations stimulated by advances in digital technologies such as artificial intelligence, big data analytics, cloud computing, and the Internet of Things (IoT). Presently, there is a lack of studies to map digital transformation in the environmental sustainability domain. This paper identifies the disruptions driven by digital transformation in the environmental sustainability domain through a systematic literature review. The results present a framework that outlines the transformations in four key areas: pollution control, waste management, sustainable production, and urban sustainability. The transformations in each key area are divided into further sub-categories. This study proposes an agenda for future research in terms of organizational capabilities, performance, and digital transformation strategy regarding environmental sustainability.