This study presents a sustainable digital transformation framework to integrate sustainable practices into digital transformation initiatives within Small and Medium Enterprises (SMEs). The methodology includes a literature review, a framework creation, and a case study with passive participation. The framework was structured to help industries implement responsible digitalization initiatives in five key stages: setting objectives, fostering stakeholder-focused engagement, defining sustainable objectives and dimensions, creating a sustainable model, and executing the project. Validating the proposal in the context of an SME allowed us to discern the tangible benefits of sustainability practices for the organization, further reinforcing the relevance and applicability of the framework. In conclusion, this research offers valuable information for SMEs considering starting a sustainable digitalization process.

Copper mining activity is going through big changes due to increasing technological development in the area and the influence of industry 4.0. These changes, produced by technological context and more controls (e.g., environmental controls), are also becoming visible in Chilean mining. New regulations from the Chilean government and changes in the copper mining industry (such as a trend to underground mining) are fostering the search for better results in typical processes such as leaching. This paper describes an experience using artificial intelligence techniques, particularly random forest, to develop predictive models for copper recovery by leaching, using data from an enterprise present in northern Chile for more than 20 years. Two models, one of them with actual operational data and another one with data generated in a controlled environment (piling) are presented. Well-classified values of 98.90% for operational data and 98.72% for pile/piling data were obtained. The methodology devised for the study can be transferred to piling columns or piles with other characteristics, though the operation must focus on copper leaching. It can even be transferred to other leaching processes using another type of mineral, with proper adjustments.

The copper mining industry is increasingly using artificial intelligence methods to improve copper production processes. Recent studies reveal the use of algorithms, such as Artificial Neural Network, Support Vector Machine, and Random Forest, among others, to develop models for predicting product quality. Other studies compare the predictive models developed with these machine learning algorithms in the mining industry as a whole. However, not many copper mining studies published compare the results of machine learning techniques for copper recovery prediction. This study makes a detailed comparison between three models for predicting copper recovery by leaching, using four datasets resulting from mining operations in Northern Chile. The algorithms used for developing the models were Random Forest, Support Vector Machine, and Artificial Neural Network. To validate these models, four indicators or values of merit were used: accuracy (acc), precision (p), recall (r), and Matthew’s correlation coefficient (mcc). This paper describes the dataset preparation and the refinement of the threshold values used for the predictive variable most influential on the class (the copper recovery). Results show both a precision over 98.50% and also the model with the best behavior between the predicted and the real values. Finally, the obtained models have the following mean values: acc = 0.943, p = 88.47, r = 0.995, and mcc = 0.232. These values are highly competitive when compared with those obtained in similar studies using other approaches in the context.

High‐speed machining is a technique that maintains a high interest in the manufacture of metal parts for the excellent results it provides, both in surface finish and in economic benefits. In the industry, the tendency is to incorporate data management and analysis techniques to generate information that helps improve the surface roughness results in machining. A good alternative to improve the surface quality results in the manufacture of metal parts is using predictive models of the surface roughness. In this document, we present work done with experimental data obtained from two high‐speed machining (HSM) machines with different types of tools and cutting conditions, conducted under an experimental design with interest in three of factors commonly studied to generate surface roughness models: tool characteristics, cutting conditions, and characteristics of the machined material. Steel and aluminum alloys were used in the experimentation. The results are contrasted with prior experiences that use the same experimental design but with different soft computing techniques and they are also contrasted with the results of similar previous works. Our results show accuracies ranging from 61.54% to 88.51% on the datasets, which are competitive results when compared with the other approaches. We also find the axial cut‐depth is the most influential feature for the slots datasets and the hardness and diameter of the cutting tool are the most influential features for the geometries datasets.

Chilean mining is one of the main productive industries in the country. It plays a critical role in the development of Chile, so process planning is an essential task in achieving high performance. This task involves considering mineral resources and operating conditions to provide an optimal and realistic copper extraction and processing strategy. Performing planning modes of operation requires a significant effort in information generation, analysis, and design. Once the operating mode plans have been made, it is essential to select the most appropriate one. In this context, an intelligent system that supports the planning and decision-making of the operating mode has the potential to improve the copper industry’s performance. In this work, a knowledge-based decision support system for managing the operating mode of the copper heap leaching process is presented. The domain was modeled using an ontology. The interdependence between the variables was encapsulated using a set of operation rules defined by experts in the domain and the process dynamics was modeled utilizing an inference engine (adjusted with data of the mineral feeding and operation rules coded) used to predict (through phenomenological models) the possible consequences of variations in mineral feeding. The work shows an intelligent approach to integrate and process operational data in mining sites, being a novel way to contribute to the decision-making process in complex environments.