In the era of digitalization, many technologies are evolving, namely, the Internet of Things (IoT), big data, cloud computing, artificial intelligence (IA), and digital twin (DT) which has gained significant traction in a variety of sectors, including the mining industry. The use of DT in the mining industry is driven by its potential to improve efficiency, productivity, and sustainability by monitoring performance, simulating results, and predicting errors and yield. Additionally, the increasing demand for individualized products highlights the need for effective management of the entire product lifecycle, from design to development, modeling, simulating, prototyping, maintenance and troubleshooting, commissioning, targeting the market, use, and end-of-life. However, the problem to be overcome is how to successfully integrate DT into the mining business. This paper intends to shed light on the state of art of DT case studies focusing on concept, design, and development. The DT reference architecture model in Industry 4.0 and value-lifecycle-management-enabled DT are also discussed, and a proposition of a DT multi-layered architecture framework for the mining industry is explained to inspire future case studies.

Transparent conducting oxides such as ZnO doped with Al or Mg are commonly used in solar cells, light emitting diodes, photodetectors, and ultraviolet laser diodes. In our work, we focus on a comparative study of the structural, optical, and electrical properties of ZnO films highly doped with Al (AZO) and Mg (MZO). These films are deposited on glass substrates by the sol-gel spin coating method. The doping concentrations for Al and Mg are fixed to 5%–30%. The XRD spectra indicate that all the samples are polycrystalline with hexagonal wurtzite structures, exhibiting a preferred orientation along the (002) plane. Low degradation in crystallinity was observed for MZO even at a Mg concentration of 30%. The MgO phase started to appear compared to Al-doped layers where smaller grains are formed inducing a deterioration in the films just after doping but no new phase appeared. This result is in agreement with other experimental results [J. K. Rath, Sol. Energy Mater. Sol. Cells 76, 431–487 (2003); Morris et al., J. Appl. Phys. 67, 1079–1087 (1990)]. By AFM analysis, the results indicate a significantly rough surface for MZO compared to AZO films. For equal Al and Mg dopant concentrations, we observe that the transmittance spectra of MZO thin films are wider than those of AZO, indicating a shift toward shorter wavelengths with an optical gap energy equal to 3.67 eV. The electrical measurements of AZO and MZO thin films were made using the I–V characteristic obtained by the four probe method. All the films present an ohmic behavior. The conductivity and the mobility of AZO films were found to be better than those of MZO.

This paper presents a systematic and comprehensive mathematical model for alkaline water electrolyzer cells, which can be used for simulation and analysis. The model accounts for factors such as gas evolution reactions, dissolution of gases in the electrolyte, bubble formation, and charge transport. It is based on a numerical two-phase model using the Euler-Euler approach, which has been validated against experimental data for various current densities. The study compares the impact of varying potassium hydroxide (KOH) concentration, separator porosity, and electrolyte flow rates on two-phase flow and bubble coverage. Therefore, the electrolyte in the cell consists of a solution of potassium hydroxide in water. The formation of gas bubbles at the electrodes decreases the electrolyte’s ionic conductivity. Additionally, the presence of these bubbles on the electrode surfaces reduces the available surface area for electrochemical reactions, leading to an increase in the overpotential at a given current density. Furthermore, this paper demonstrates how a neural network and ensembled tree model can predict hydrogen production rates in an alkaline water electrolysis process. The trained neural network accurately predicted the hydrogen production rates, indicating the potential of using neural networks for optimization and control of alkaline water electrolysis processes. The model has an average R-squared value of 0.98, indicating a good fit to the data. A new method of describing bubble transfer, “bubble diffusion,” is introduced to improve performance and reduce costs. The model is solved using COMSOL Multi physics 6.0. The machine learning models in this study were built, trained, and tested using MATLAB software R2020a.