Overview
Dig_IT objectives
Our Project
Dig_IT project aims to develop a human-centred IIoT platform [O1] connecting the mining ecosystem of assets, environment, and humans [O2-4] to increase mining efficiency: saving costs using optimised scheduling [O8], increasing uptime using predictive operation and maintenance [O7], identifying new revenue opportunities using advanced geological interpretation on exploration mining phase [O6a]. To address industry needs of minimizing accidents [O4,9,10], optimising production processes and reducing costs, [O6-8,10] intelligent systems will provide real-time insights [O6d, 7-10] for the enterprise at all operational levels.
Dig_IT follows a market need & technology offer approach aiming at covering all aspects of technical, industrial and business requirements towards a sustainable future in mining. The project’s value chain and concept has been built with the utmost objective to provide new solutions addressing the needs for safety, efficiency and sustainability, bringing innovative and competitive solutions to the mining business, face future challenges regarding standards and legislation, and spread the knowledge to as many sectors of the European extractive industry as possible.
The impact of Dig_IT to the European Mining industry, but also the society itself, can be summarised in the following (with a horizon of 4 years after project ends): (i) increase of the mining efficiency by 17%, (ii) increased OEE for machines and loading by 20% and 18% respectively, (iii) 19% reduction of CO2eq, (iv) about 310 new jobs created and (v) over 28M EUR ROI for the consortium.
Main Objectives
Objective 1
To design and validate a secure, smart Industrial Internet of Things platform (IIoTp) that will improve the efficiency and sustainability of mining operations by connecting cyber and physical systems (WP2) with a focus on the Edge Control layer (WP3) according to industry needs (WP1). The platform will rely on a micro-services-based architecture, able to support the use of various end-devices (including environmental sensors, personnel biometrics sensors) or datasets, e.g. the option of integrating legacy SCADA-based data as well as historical datasets on sustainability or commodity market (WP2, WP6). The IIoTp will provide orchestration among different north-bound applications via flexible APIs (WP4, WP5, WP6) and integrate all respective Dig_IT sub-systems (O2-10).A Cyber Security Layer will ensure the secure communication and integrity.
Objective 2
To achieve on-line measurements of asset-bound mining operations (i.e. drilling, mucking, hauling, stocking, crushing, milling, sawing) through monitoring critical asset (i.e tools, machines, vehicles) condition and operation in respect with their operational and environmental output (WP2).
Objective 3
To achieve O3a) online distributed measurements of same or higher accuracy compared to current methods used today for broad area sustainability measurements and O3b) occupational work environment large scale on-line stationary measurements. The output will result in: (i) distributed measurements (air, water, noise) at three points in the surrounding environment of the mines; (ii) at least three distributed on-line occupational measurements of noise and air quality specific for the end users; (iii) data quality of the measurements performed in (i) & (ii) within 5% of the values given for standard testing (WP2)
Objective 4
To develop a Smart Garment (WP2) for mining personnel sensing Occupational Health, Safety and Environmental (OHSE) parameters, biometrics, and situational awareness with an accuracy greater than 95% compared to commercial bulkier devices. Tethered in-ear headphones able to recognise speech using artificial intelligence will support audio guidance in at least twenty Q&A sets in five different languages.
Objective 5
To achieve Big Data optimisation through improving data quality aiming to minimise unused data and increase accuracy of analysis (WP3). Dig_IT data will undergo a process of eliminating Bad Data collected during operation due to various reasons (i.e terrain obstacles, communication failures etc.). This optimisation will contribute to achieve real-time insights.
Objective 6
To develop Digital Twins (DTs) of the physical mine entities, systems and processes to gain insights for respective mining operations (WP4). O6a) Geotechnical DTs of terrain, structural behaviour, geological interpretation to predict stability risk areas; O6b) Fluid dynamics DTs of ventilation, blasting and water modelling to predict and map risk areas; O6c) Assets DTs to predict and optimise production, planning, execution, fleet management, condition monitoring and achieve physics-based predictive operation and predictive maintenance outputs; O6d) DTs will be converted into real-time DTs using advanced algorithmic tools.
Objective 7
To develop O7a) a predictive operation system and O7b) predictive maintenance agents with increased fidelity and accuracy of 95%. Advanced data analytics and machine learning algorithms will be deployed for process optimization and predictive maintenance of equipment, detecting deviations from the normal states (anomalies) with 95% accuracy. (WP5).
Objective 8
To develop a Smart Scheduling framework for mining to allow optimisation on all mining related processes regarding resources, operations, and production: i.e. energy, water, waste, emissions, ventilation, routes, cooling with a target reduction of 10% for all. This will be achieved through: O8a) a novel algorithm for phase-wise optimization problem (POP) incorporating cost-/service-oriented KPIs, sustainability-oriented measures and a novel computational framework for phase-based evolutionary computation; O8b) a fast and scalable Smart Scheduling Tool (SST) to support decision-making; O8c) impact evaluation of SST on sustainability of mining operations using LCA methodology in comparison with the current practice.
Objective 9
O9a) To develop an Intelligent Toolbox for OHSE to support operational awareness and decision-making based on biosignal analytics, air-quality forecasting, spatiotemporal person & asset tracking and early warning smart insights (WP5); O9b) to develop a safety culture rule-based methodology for IIoT to determine conditions of potential accident risk in the workplace taking under consideration European Agency for Safety and Health at Work (EU-OSHA) standards and respective National Legislation frameworks. This methodology will provide the design requirements for the Intelligent Toolbox (O8a) and further input for the assessment and determination of mining staff training needs (WP1). Ethical and legal frameworks for biometrics data used within the mining workplace will be explored.
Objective 10
To develop a Decision Support System (DSS) integrating data streams, existing subsystems, data analysis outcomes (O2, O3, O5), and Dig_IT intelligent systems (O4, O6, O7, O8, O9) using bespoke visualization of processes, objects and parameters for Dig_IT users (WP5). The system aims at increasing the sustainability of the mining operations at all 95% accuracy (WP5).
Objective 11
To validate (TRL5) the Dig_IT platform (WP6) on five industrial use cases in Europe of diverse mine lifecycle phases, mining methods, and technology adoption levels, thus framing a challenging map of use-case scenarios according to industry needs (WP1): i) a reopening open-pit tungsten (Critical Raw Material) mine in parallel planning and production underground operations (ES); ii) an open-pit iron ore, gold and copper mine brownfield project currently in permitting phase (FI); iii) a large mining enterprise with ongoing contracts in numerous underground mines focusing on Sotkamo mine operations (FI); iv) an open-pit ilmenite mine operating near a very active community aiming to share environmental data towards establishing trust with the local community (NO); v) an underground quarry of marble dimension stone (IT).
Objective 12
Dig_IT aims to address the society (WP7). O12a) on a communication and transparency level sharing trusted sustainability data using blockchain and distributed ledger technologies (DLT) within local mine communities; O12b) on an academic and institutional level producing high quality research, participating in prestigious events and forming an advisory board; O12c) on an enterprise level, addressing extractive industry networks worldwide; O12d) on an ecosystem level aiming to cluster with other projects financed under this topic and other relevant H2020 projects in the field, reaching out for project alliances for the sustainable production of raw materials in support of the European Innovation Partnership (EIP) on Raw Materials.