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Grinding and Flotation Optimization Using Operational Intelligence

In recent years, metal-producing companies have increased their investment in automation and technological innovation, embracing new opportunities to enable transformational change. Transformation to a digital plant can fundamentally revolutionize how industrial complexes operate.

The abundant and growing quantity of real-time data and events collected in the grinding and flotation circuits in a mineral processing plant can be used to solve operational issues and optimize plant performance. A grade recovery model is used to identify the best operating conditions in real time. The strategy model is used to identify the best operating conditions in real-time for increasing the value of instrumentation in current plants.

An optimal Gaudin size distribution model provides augmented information from traditional sensors to find the optimal grind cut size to reduce metal losses in the flotation circuits. At the same time, sensors in flotation circuits enable an estimate of the recovery and determination of the optimal froth depth and aeration using an air hold-up flotation model. A strategy of classifying data for online generation of insights to using operational intelligence tools is described. The implementation of a recovery/grind strategy with industrial examples in non-ferrous mineral processing is presented.

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Collaboration at the Enterprise using Real Time Data Analysis: From Data to Action

Intelligent System (IS) technologies are receiving much attention in a wide range of process engineering applications from plant operations to enterprise-wide competence centers. The application of rapidly changing technologies has become a serious challenge to both management and technical teams. Data captured from diverse real-time devices, manufacturing assets, and transactional systems, can be aggregated into information and structured by context for its use. Transforming real-time data into information requires special algorithms and filtering techniques. This real-time information enhances the collaboration between working groups (Operations, Maintenance, Process Engineering, and Management). Simple access to operational data with analytical tools enables then the personnel to identify the root cause, avoid costly constraints, and try new ideas. This paper describes the data hierarchy required to support analytical performance improvement. Typical applications are energy and water management, condition-based maintenance, quality monitoring, statistical process control, multivariate analysis, data reconciliation, and process debottlenecking. Integrated information is shared via the Web, which fuels innovation towards continuous improvement at the local and enterprise level

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Metallurgy Analytics: Transforming Plant Data Into Actionable Insights

The advent of the digital revolution has now enabled us with numerous tools that could be leveraged to transform our operational data into actionable insights. Key opportunities with digitization include better visualization, transparency, integrated planning and execution for value-chain optimization, which results in smarter production, intelligent response to changes in ore, process and equipment conditions, reducing energy and waste, along with prevention of asset breakdown, safety and environmental issues. It is important to realize that these digital tools have limited value, from an ametallurgical operational context, if we cannot bring in the appropriate domain expertise along with getting the basics right.
The focus within Barrick is to ensure that there is adequate depth of different disciplines built into our platforms, along with breadth to integrate other disciplines such as geology and mining for an effective Mine-to-Mill integration. A gold recovery improvement strategy based on optimal Gaudin size distribution, leaching performance monitoring, and guidance via feed grade maps is discussed. Simultaneous identification of process and equipment constraints enables finding the best overall conditions for Gold recovery.
This paper discusses the methodology, findings, and challenges in the ongoing journey of implementing a Metallurgy Analytics platform that evolves from being retrospectively descriptive to anticipatively prescriptive.

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Dynamic Model-Based Flotation Performance Monitoring

Froth flotation is widely used for the separation of particulate materials in the world. It is a complex process whose operational behavior in process plants is difficult to predict. This model uniquely linked the particle/bubble interactions and water transport phenomena to the flotation cell hydrodynamics.
The model was designed as a dynamic framework to analyze and develop process optimization strategies. The dynamic flotation model has several manipulated variables (such as feed flow rate, % solids, aeration, wash water flow rate, frother flow rate, specific power energy (impeller speed/delta pressure), cell geometry and tails flow rate) and evaluates the dynamic effects of these variables on grade and recovery.

The capabilities of the model predictive capabilities can be used for online sensing of the flotation performance and early identification of faults. Such an implementation of the current process analysis tools and a simplified model is presented, to demonstrate how this new environment can be used to interactively experiment with flotation parameters. New developments in mineralogical characterization, real-time information management systems, and data validation simplify the implementation of flotation technologies for mineto mill grade recovery optimization. With recent developments in software programming, dynamic simulation enables process engineers to develop process alternatives interactively.

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Grade Recovery Optimization Using Data Unification and Real-Time Gross Error Detection

The efficient operation of any modern metallurgical facility depends on the accurate measurement and estimation of flow rates, inventories, and the composition of their intermediate and final products. All of this information is subject to both statistical errors and gross errors, which can lead to poor estimation of efficiency, yields and specific energy consumption.

The detection and the elimination of these errors require not only plant data, but also product transactions and the operational events. Once the gross errors have been eliminated, it is possible to estimate validated performance indicators, such as grade recovery or yields.

This paper describes the methodology to implement a unification and gross error detection system using the available infrastructure of both information and data reconciliation systems. It covers the different infrastructure layers, from the data collection layer to the data validation and reconciliation layer.
In addition, the impact of these methodologies on the decision-making process is presented.
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Maximizing copper production by proper water management using a Digital Twin

Mineral processing plants processing low-grade ores require large amounts of energy and water to operate in a sustainable and profitable state. These ores present large variations in their mineralogy, metal content and hardness. These low-grade ore plants process mostly rock in the first part of the process, followed by traditional mineral processing and water recovery systems. Currently, mineral processing plants operate in silos and lack the necessary integration of data from mining, grinding, classification, flotation, thickening, and tailings processing. The process information with the right degree of detail is missing for understanding the integrated process. Today, tight coordination between the mining product, grinding, classification, flotation, and water recovery processing is a must.
A novel strategy using a Digital Twin was designed to increase the necessary water recovery from the thickeners and tailings ponds to maximize the copper production rate in a low-grade ore industrial plant. The correct shape of the grinding particle size distribution is monitored to improve both the flotation metal production rate and the flocculation of the tails produced in the rougher flotation. The plant data model consists of a Rock Processing, a Water Processingwhich integrates the plant to find the best operating conditions that optimize the copper production rate based on the plant schedule.
The implementation of the Digital Twin results are a 40% increase in water recovery for an increase of copper production rate by 32%. These savings are very significant based on zero-capital investment requirements using their actual process historian data infrastructure. The OSB Digital Twin is based on the implementation of an integrated mineral processing plant model built using the PI System and OSB Grinding, Flotation and Thickening dynamicsimulators. The critical process operational modes are calculated based on the plant business plan and current data to transform the time series raw data into information to build the necessary machine learning models. These models are used to understand the integrated behavior of the plant and to avoid violating costly process constraints in the grinding, classification, flotation, and thickening processes.

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Measuring, Managing, and Maximizing Refinery Performance

Implementing continuous quality improvement is a confluence of total quality management, people empowerment, performance indicators, and information engineering. Supporting information technologies allow a refiner to narrow the gap betweenmanagement objectives and the process control level. Dynamic performance monitoring benefits come from production cost savings, improved communications, and enhanced decision-making. A refinery workgroup information flow model helps automate continuous improvement of processes, performance, and the organization.

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Real-Time Information Management Infrastructure – Collaboration at Mine-Mill for Asset Optimization

Recent advances in industrial automation require a new approach to get plants to reduce their start-up times and adapt to the varying ore types. Integration of many subsystems is a requirement for improved operational management in metallurgical complexes' operations. For example, drastic changes in ore type at the mine require a proactive evaluation for the optimal crushing requirements for improved leaching of the ore. Access to the internet and faster networks are assisting in providing high visibility of the operations. Then, gathering the information for real-time analysis and presentations on the web is changing the way that we operate and collaborate. The implementation of competences centers is now feasible to collaborate at the enterprise level.
competence.
This paper will detail some innovative ways of organizing the information for improved overall performance. The integration of the information enables the calculation of indices to track in details the operations and equipment availability. Examples in mineral processing will be presented, and another on a metallurgical mass balance is proposed. The major benefits are increased overall process equipment effectiveness, reduction of organic losses, and improved energy and quality management of the cathodes. The use of the portal for collaboration in the large metallurgical complexes will also be discussed.

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Measuring, Managing and Maximizing Performance in Petroleum Refineries

The implementation of continuous quality improvement is the confluence of management quality, people empowerment, the use of performance indicators, and information engineering. The supporting information technologies allow a refiner to narrow the gap between management business objectives and the process control level. Dynamic performance monitoring benefits come from production cost savings, improved communications and enhanced decision making. A refinery workgroup information flow model is presented to help automate continuous improvement of processes, performance, and the organization.

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Operational Decision Support for Low-Grade Mineral Processing Plants

Ores are becoming extremely variable with mineralogy and hardness disturbing the integrated crushing, grinding, flotation, and thickening processes. The current grinding and flotation sensors provide large amounts of data for process optimization. To augment the operational knowledge for proactive actions for improving the performance of the grinding and flotation circuits, we need to add the right process knowledge context and operational modes. Using the latest tools and cloud computing enables the creation of new workflows and collaboration between mining, concentrator plants, and the enterprise, including service providers. Machine learning pervades our culture in a multitude of ways, from medical diagnosis and data management to speech synthesis and search engines. Today, subject matter experts (SMEs) can increase productivity by developing predictive models to classify the operating conditions owing to large variations in ores, catching the hidden production, energy, and water losses by ore type and unmeasured disturbances.

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