BrainMap

Ovidiu Eugen Avram

- INSTITUTUL GEOLOGIC AL ROMANIEI

Researcher

Expertise & keywords

Artificial Intelligence and Combined Survey Techniques for Stone Quarries Optimization Call name: P 3 - SP 3.2 - Proiecte ERA.NET - COFUND
COFUND-ERAMIN-3-AI-COSTSQO-2 2022 - 2024

Role in this project:

Coordinating institution: INSTITUTUL GEOLOGIC AL ROMANIEI

Project partners: INSTITUTUL GEOLOGIC AL ROMANIEI (RO); BAY E Bilişim Danışmanlık Eğitim Bilgisayar Sanayi ve Ticaret Limited Şirketi (TR); Faculty of Information Studies in Novo mesto Complex systems and Data Science Lab (SI); UNIBO - University of Bologna DICAM - DEPARTMENT OF CIVIL, CHEMICAL, ENVIRONMENTAL AND MATERIALS ENGINEERING (IT)

Affiliation:

Project website: https://www.ai-costsqo.igr.ro/

Abstract:

ABSTRACT

Stone blocks, such as marble, granite, and sandstone, are natural materials with excellent properties. Their market is currently worth 30 billion EUR, and is increasing day by day, since they are in high demand in a variety of industries. However, their exploitation in quarries is conditioned by two unfavourable factors. First, these ores have naturally occurred fractures and discontinuities, such faults, joints, and fissures, which preclude the excavation of larger blocks with bigger commercial value. Second, stone quarrying is a very intensive waste-producing process, since not all excavated material is of commercial use. With booming markets, risks to the environment are only bound to become more serious. Here, we propose the project to ameliorate the above situation. We will combine geophysical and photogrammetric survey methods to populate a database with all-encompassing inventory of major European quarrying sites. These in-situ, non-invasive measurements will include the Unmanned Aerial Vehicles equipped with Light Detection and Ranging sensors, Ground Penetrating Radar, Borehole Digital Imaging, and Integrated data Processing of the Point Cloud. They will be used to detect the discontinuity surfaces in the natural stone and formulate a polygonal model. Next, the polygonal model will be examined via an innovative Artificial Intelligence approach. The identification of the discontinuity sets will be improved by the actual “visual” manually operated procedure. With help of Deep Learning algorithms this will lead to a higher degree of automation. The derived Discrete Fracture Network will be considered as input for the polyhedron extraction methodology and computational optimization. These innovative algorithms consist of the combined use of several parameters like maximum cuboids volume, the surface of slabs, the orientation of the cutting grid in quarries, and commercially relevant parameters. In turn, this will enable the framework for automated design and optimization of mining plans, tailored to specific mine and stone properties, maximizing the size of the extracted stone blocks. This will increase the efficiency of raw materials supply while creating environment friendly and sustainable modus of exploitation. In addition, this should reduce the waste production, while consuming less energy and water. The developed model will be validated for each quarry site and will be applied to the existing quarries. The most important impact of this undertaking will be recovering up to 25% of waste. Completion of this project amounts to a considerable technological improvement in mining of natural resources and reduction of environmental risks, thereby boosting the competitiveness of Europe.

PUBLISHABLE ABSTRACT

AI-COSTSQO partners come together to create an eco-efficient and sustainable stone exploitation by using non-invasive survey, optimize production, reducing waste, energy and water usage. Thus, the degree of negative social effects of mining activities, which have increased in recent years, will also be reduced. The interest area of the project will cover both currently operating mines and non-operating deposits. With the project work, an effort will be made to evaluate the current situation, to predict the financial profitability of virgin deposits and the amount of waste to be produced. The project, which has an interdisciplinary character, consists of academic people who are experts in their fields. These people have a lot of projects and academic studies related to the subject, and they are completely locking at the target. The operability of the stone deposit as a rock mass is mostly assessed by the presence of discontinuities. Our project will be primarily the basis on the modelling of the existence of these using Analytical, Mathematical, Statistical, Machine Learning and Big Data solutions. In addition, realistic survey methods will be used as mainly data. The innovative model will be created combining several approaches, like calculating the maximum cuboid volumes that fit into natural polyhedrons and the orientation of the cutting grid, considering discontinuities and planning to cut directions and spatial position of general planning of quarry using block dimension distributions. In this concept, six work packages have been designed and distributed to the partners according to their specialisation. Although there is no exact data for natural stone quarries recovery rates, it is well known these rates may be decreased to about 10% in many quarries. We believe that the project outcomes, combined innovative survey methods and new optimization algorithms, will significantly improve the recovery rates and decrease waste production in stone quarries.

FILE DESCRIPTION	DOCUMENT
List of research grants as project coordinator or partner team leader
Significant R&D projects for enterprises, as project manager
R&D activities in enterprises
Peer-review activity for international programs/projects