Intelligent Risk Analysis of Investment Projects in the Extractive Industry
Journal Title: Journal of Industrial Intelligence - Year 2024, Vol 2, Issue 1
Abstract
This study introduces an advanced technology for risk analysis in investment projects within the extractive industry, specifically focusing on innovative mining ventures. The research primarily investigates various determinants influencing project risks, including production efficiency, cost, informational content, resource potential, organizational structure, external environmental influences, and environmental impacts. In addressing the research challenge, system-cognitive models from the Eidos intellectual framework are employed. These models quantitatively reflect the informational content observed across different gradations of descriptive scales, predicting the transition of the modelled object into a state corresponding to specific class gradations. A comprehensive analysis of strengths, weaknesses, opportunities and threats (SWOT) has been conducted, unveiling the dynamic interplay of development factors against the backdrop of threats and opportunities within mineral deposits exploitation projects. This analysis facilitates the identification of critical problem areas, bottlenecks, prospects, and risks, considering environmental considerations. The application of this novel intelligent technology significantly streamlines the development process for mining investment projects, guiding the selection of ventures that promise enhanced production efficiency, cost reduction, and minimized environmental harm. The methodological approach adopted in this study aligns with the highest standards of academic rigour, ensuring consistency in the use of professional terminology throughout the article and adhering to the stylistic and structural norms prevalent in leading academic journals. By leveraging an intelligent, systematic framework for risk analysis, this research contributes valuable insights into optimizing investment decisions in the mining sector, emphasizing sustainability and economic viability.
Authors and Affiliations
Abdullah M. Al-Ansi, Askar Garad, Vladimir Ryabtsev
Keywords
Related Articles
- EP ID EP743949
- DOI 10.56578/jii020104
- Views 11
- Downloads 0
Benefits and Challenges of Implementing Autonomous Technology for Sustainable Material Handling in Industrial Processes
The transition from traditional production activities to a manufacturing-dominated economy has been a hallmark of industrial evolution, culminating in the advent of the fourth industrial revolution. This phase is charact...
Numerical Analysis of Viscosity and Surface Tension on Microdroplet Dynamics in Microelectromechanical Systems Applications
Microelectromechanical systems (MEMS) have instigated transformative advancements, notably in controlled microdroplet generation, offering applications across diverse industrial sectors. Precise control of fluid quantiti...
Enhancing Green Supply Chain Efficiency Through Linear Diophantine Fuzzy Soft-Max Aggregation Operators
Improving the effectiveness of green supply chains is a critical step towards minimizing waste, optimizing resource use, and reducing the environmental impact of business operations. Sustainable practices should be imple...
Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach
Amid the COVID-19 pandemic, the imperative for alternative biometric attendance systems has arisen. Traditionally, fingerprint and facial recognition have been employed; however, these methods posed challenges in adheren...
Enhanced Signal Processing Through FPGA-Based Digital Downconversion via the CORDIC Algorithm
To address the rate matching issue between high-bandwidth and high-sampling-rate analog-to-digital converters (ADCs) and low-bandwidth and low-sampling-rate baseband processors, the key technology of digital downconversi...