INTRODUCTION

Digital systems for Process Simulation first emerged in the 1970s and have since played an increasingly important role within every phase of the mineral process plant project life cycle. Today, it is possible to accurately model and simulate large-scale processes with complex behaviour, chemistry and thermodynamics in steady-state and dynamic operation. Shown in Figure 1 is the simplified framework of a typical Simulation system, highlighting the key differences between steady-state and dynamic capabilities.

Figure 1: Typical Simulation System with Steady-State and Dynamic Capabilities

In this paper, we investigate the status quo of current Process Simulation best practices within the Design and Operations phases of a project life cycle of a process plant and hypothesise how the future impact of Digital Transformation will see new opportunities and best practices emerge.

PROCESS SIMULATION IN A PROJECT LIFE CYCLE

The Many Roles of Process Simulation in the Life Cycle of a Process Plant

Illustrated in Figure 2 are the typical phases of the project life cycle of a process plant, together with a selection of key Process Simulation uses and roles during each phase. Throughout this paper, “Design phase” represents the Feasibility Study, Engineering Design and Plant Expansion phases, while “Operations phase” includes Plant Operation and Plant Maintenance. Commissioning often incorporates elements from both Design and Operations, including model handover from Design to Operations teams.

Figure 2: Typical Roles of Process Simulation in the Life Cycle of a Processing Plant

 

STATUS QUO OF PROCESS SIMULATION IN A PROJECT LIFE CYCLE

Current Practices in the Design Phase

Process engineers generally use Process Simulation tools, such as SysCAD, in the Design phase, especially during Feasibility Study and Engineering Design, by developing simulation models based on similar design knowledge and initial test work. Different process flowsheet arrangements and process unit configurations are evaluated using these modelling platforms.

Once simulation flowsheets are created, including configuration of equipment operating conditions and process chemistry settings, the simulations are refined to achieve the process mass and energy balance which will become the design basis for the project. This typically steady-state model is iteratively improved based on laboratory sample assays, pilot plant test work, equipment supplier data and process engineering knowledge from similar projects; in effect becoming a repository of process design knowledge. This design basis is then used by process engineers in defining the Process Design Criteria (PDC) for the project.

The status quo in process modelling is that while the simulation system is primarily used by process engineers for Design, it is often not integrated or accessed by other engineering disciplines or engineering packages. Digital integration, which is performed, for example to connect with third-party predictive chemistry models, is often done on an ad-hoc or project-specific basis.

Process Flow Diagrams (PFDs) are typically CAD drawings created by drafts people using drawing standards and symbols. Process engineers often provide the key process data for the process streams shown in the PFDs in a spreadsheet report to be incorporated in the PFD deliverables of the Design phase.

The simulation database and models, including both steady-state and dynamic simulations, are maintained by the process engineers during the Engineering Design phase to align with the detailed design, sizing and selection of equipment and process control systems. This information is incorporated into Piping and Instrumentation Diagrams (P&IDs) in the later stages of plant design.

Any process design changes to the PDC, PFDs and P&IDs are typically made by the process design engineers through document revision within change management control systems. This data duplication and administrative overhead often results in a low level of efficiency and can introduce errors or misalignment between process models and design documents.

The process simulation toolset is often considered solely a process design system, and the simulation database and models are typically not a contractual deliverable from the Design phase to Operations.

Current Practices in the Operations Phase

Process plant metallurgists and onsite process engineers often use process simulation in the Operations phase of a project life cycle for a wide range of applications. These include development of a better understanding of the process, identification of bottlenecks, metallurgical accounting, improved reagent usage, reduced energy use, evaluation of process upsets, improvement of production and product quality, and identification and evaluation of opportunities to optimise and maintain their process plant.

The PDC, PFDs and P&IDs from Design are typically used as inputs to configure a chosen process simulator. These may be steady-state or dynamic models and the fidelity differs depending on the focus of the modelling work. Models may be plant-wide including utilities and process areas, or specific to a detailed sub-area.

Unlike during the Design phase, plant data is available to tune the model in Operations. Using plant data, these models are refined and modified to best match the real-world process and equipment.

After optimisation studies or process improvements have been achieved, these Operations process models are often not maintained to match the changing real-world conditions. Together with staff turnaround, these models often fall into disuse until the next project. Organisation may not allocate resources to maintain models for future use or create systems where process models are an integral part of operational decision support. The net result is diminished use and value of the process simulation system in Operations.

While key outcomes from short-term modelling work are often reported and actioned, secondary technical insights identified during project work may be lost along with the associated models, ultimately leading to future rework and missed opportunities.

DIGITAL TRANSFORMATION

Digital Transformation is occurring throughout the full project life cycle. Design teams, including Engineering, Procurement and Construction groups (EPCs), are increasingly adopting holistic integrated Building Information Modelling (BIM) systems. Meanwhile, Operations teams are adopting Industry 4.0 standards and Digital Twin systems, mirroring the real-world as shown in an excerpt from the DECHEMA and ProcessNet⁽¹⁾ position paper in Figure 3. Here, the Real World Processes (Asset Lifecycle and Supply Chain) represent the intersecting of the Design and Operations phases of the project life cycle, and the Digital Twin reflection also incorporates data-centric BIM systems.

Figure 3: Digital Transformation in Design and Operations (DECHEMA and ProcessNet⁽¹⁾)

Digital Transformation in the Design Phase

With a well-designed and integrated BIM system, it is possible for all members of a multidisciplined Design team, from project management and engineering through to manufacturing suppliers and construction, to securely access the Design phase systems and data via a shared project intranet from anywhere in the world, on any device.

BIM systems are an emerging form of data-centric electronic documentation, typically for buildings and physical infrastructure, containing augmented design models with remote system functionality. These additional levels of detail are referred to as “dimensions”, including:

• 2D & 3D Intelligent Drawings and Models

• 4D Time and Schedule attributes

• 5D Cost and Commercial attributes

• 6D+ Simulation, Life Cycle, Sustainability and Value Engineering

Process Simulation and Modelling systems are an integral part of BIMs being used for mineral process plant design. For example, we are increasingly seeing the integration of dynamic rainfall and weather datasets (4D) in pond and dam unit operation models to visualise their impact over time. Additionally, raw material costs (5D) are being applied in unit operations that consume reagents, forecasting the OPEX impact of various alternative design scenarios.

Although the exact hierarchy of higher-order dimensions (6D+) is not generally agreed upon, common themes include full project life cycle analysis, with a focus on sustainability and circular economy, as well as facility management systems, and value engineering optimisation strategies. Each of these components rely on detailed simulation and modelling for design and to quantify their impact.

Digital Transformation in the Operations Phase

The nine pillars, or key enabling technologies, of the fourth industrial revolution (Industry 4.0) are:

• Additive Manufacturing

• Augmented Reality

• Autonomous Robots

• Big Data and Analytics

• Cloud Computing

• Cybersecurity

• Horizontal and Vertical Integration

• Industrial Internet of Things (IoT)

• Simulation

Process modelling is a cornerstone of Industry 4.0. Simulation techniques are being used in Operations to leverage real-time and near-real-time plant data to mirror the physical world in so-called Digital Twins. When done correctly, these simulations allow Operations teams to test and optimise process settings in various “What If?” scenarios, manipulating and testing the Digital Twin rather than the real-world process plant.

Results from these Digital Twin simulations have immediate payback, including hazard reduction and improved on-site safety, reduced maintenance times, optimised raw material consumption and improved production.

Figure 4: Industry4.0 and Digital Twins in Operations

Transition from Design to Operations

Despite ongoing Digital Transformation in both spaces, the transition from BIM Design systems to Industry 4.0 and Digital Twin Operations systems is not well understood. In many cases these are not the same systems or datasets and require transformation from Design models to user interfaces, analytics and output visualisations suitable for Operations.

At the completion of the Design phase, the BIM data represents the “as designed” nameplate capacity of the process plant. Once the plant equipment is commissioned, operations commence and process materials, reagents and utilities are introduced, the process plant begins to degrade. As well as physical maintenance, this also necessitates an ongoing maintenance of the simulation models.

The Industry 4.0 and Digital Twin Operations systems must be updated to “as maintained” as the actual plant performance deviates from nameplate capacity (e.g. accounting for changes in feed minerology, and flowrate and vessel capacities due to wear and scaling). This is done such that the Digital Twin continues to mirror the physical operating plant, thereby providing a valuable simulation tool throughout the Operations life cycle.

Figure 5 shows some practical examples of systems transformation from Design into Operations. During this transition phase, Design systems are cleaned of redundant or design-only data, and the data is transformed to support the Operations systems. This often also represents a shift towards an increased focus on dynamic modelling.

Figure 5: Simulation Systems Digital Transformation from Design into Operations

FUTURE SIMULATION BEST PRACTICES IN DESIGN AND OPERATIONS

Future Best Practices in the Design Phase

Through widespread adoption and implementation of BIM systems, of which Simulation is an integral component, common process design data would be available for access and digital integration across many of the engineering Design phases of the project life cycle.

In this way, relevant process data is captured once and shared across multiple systems, resulting in more efficient and consistent quality design data. Process Simulation systems will support integrated visualisation of process data for intelligent PDC, PFDs and P&IDs, as well as parallel development of multidisciplinary engineering documentation such as construction 3D models including equipment, instrumentation and valve data sheets.

Future process simulation toolsets will be integrated into a significant range of Automation systems, and will support HAZOP studies, configuration and testing of the process control system (PCS) and start-up/shut-down systems, as well as safety-critical device management systems prior to energisation at site.

It is envisaged that the “as designed” simulation database and models from the Design phase will form a contractual deliverable to Operations.

When major improvements or plant expansions occur during an operating plant’s life cycle, the process simulation models, which will have been managed and maintained by the Operations team, will be returned to the engineering organisation responsible for the expansion or design improvement. Again, these new simulation models will form part of the engineering design deliverables for reintegration and use in Operations.

Future Best Practices in the Operations Phase

With improved Process Modelling platform integration tools, APIs, and user-friendly “plug-and-play” software development kits, it is anticipated that extensive integration between real-world plant measurement, control systems and site-based software systems with Digital Twins will provide a flexible digital toolset for Operations.

The Simulation platform, alongside integrated Automation systems, will be used as a fully-functional and immersive Operator Training System (OTS) to train site engineers and operators for various operating scenarios.

Future process Simulation systems will be owned, managed and maintained by Operations team. The process Digital Twin component is typically owned and continuously improved by the plant metallurgists, who will, in turn, publish updates of the Digital Twin simulation models to onsite Operations systems. These systems will be accessible by the full Operations team and will provide access to applications, dashboards, reports and analytics to plan and manage process impacts, achieving improved production and maintenance of the process plant.

SIMULATION DIGITAL TWINS FOR OPERATIONS

The following section presents three practical applications of using a process Digital Twin simulation services system in Operations.

Asset Utilisation & Planning

Asset utilisation and planning using Digital Twin modelling provides many operational benefits, such as:

• Isolate and minimise plant bottlenecks

• Identify process issues and unexpected dynamic interactions early

• Optimise plant control strategies

• Manage significant disturbances, including planned and unplanned shutdowns

• Calculate and report overall and area availabilities and equipment utilisation

• Report raw material and utilities consumption, as well as management of emissions and waste

• Production planning and maintenance scheduling

• Forecast production and cost impacts for varying asset utilisation scenarios

• Track inventories, limits and related buffer timeframes impacted by process upset or asset loss

• Process equipment surge and availability studies

• Minimise production loss impact based on catch-up potential after asset recovery

• Run “What If?” scenarios for plant optimisation and future planning

A typical Asset Utilisation & Planning Digital Twin simulation system is shown in Figure 6. Here the objective is to support Operations team decisions, by visualising asset availability based on reliability and redundancy data, allowing simulation of scenarios to better define maintenance schedules and select alternate operations during plant and asset disruptions.

Figure 6: Example 1 – Asset Utilisation & Planning using Digital Twin Simulation

Energy and Carbon Footprint Visualisation

Operations teams are increasingly working towards better understanding their energy usage and carbon emissions to optimise and decarbonise their process plants. The use of Digital Twin modelling and analytics is fundamental to this understanding.

Renewable power simulation systems that allow Operations to model their power generation and micro-grids under varying time-of-day and weather conditions are well advanced and provide Operations with good visualisation and analytics to optimise and run their renewable wind, hydro, solar and battery power sources.

Although this electrical power usage accounts for a significant portion of energy use within a process plant, the fuel-driven and chemical reaction process units also significantly contribute to the total carbon footprint of a process. With an integrated process plant Simulation system, Operations teams can run scenarios for optimal heat recovery, pinch point analysis, and other energy-saving and decarbonisation opportunities.

Integrating renewable power simulation systems with a process simulation Digital Twin provides Operations with a holistic visualisation of their plant’s energy consumption and carbon footprint. This knowledge allows for

better understanding and identification of opportunities to maximise decarbonation efforts. Figure 7 provides an example of such a system, with energy usage data taken from various real-world operations sources.

Figure 7: Example 2 – Energy and Carbon Footprint Visualisation

Dry Laboratory

As described in the 2020 paper by Ghorbani et al.⁽²⁾ “Repurposing Legacy Metallurgical Data Part I: A Move Toward Dry Laboratories and Data Bank Repurposing”, there is a need for an integrated approach to achieve process value chain-wide optimisation.

The availability of integrated laboratory data, including transformation of historical assays, would enable the shift to “Dry Laboratory” environments as a future-oriented approach where process optimisation is performed within a Digital Twin backed by laboratory-confirmed data. Figure 8 shows what such a Dry Lab simulation system would look like. This Dry Lab would allow for better understanding and visualisation of process chemistry across the mineral process value chain, allowing for improved optimisation of process performance.

Figure 8: Example 3 – Dry Lab

 

CONCLUSIONS

The use of Process Simulation tools, such as SysCAD, is historically well established in Design phase of the project life cycle and has achieved some level of use in Operations. A clear understanding of the benefits that can be achieved through implementation of modelling best practices throughout the entire project life cycle has not been well defined.

The earlier Process Simulation is adopted, the sooner potential issues are identified affecting design change, and the greater the quality of simulation models for transition to use as Digital Twins. Further, the more integrated these systems, with the support of emerging digital technologies, the more accurate and predictive they become, increasing the value gained from their use throughout the Design and Operations phases.

In response to the titular question posed by the DECHEMA and ProcessNet⁽¹⁾ position paper, “Fit for the Future?”: The next generation of Process Simulation Systems will be fit for the future and are an essential component to achieve industry goals for Digital Transformation.

ACKNOWLEDGEMENTS

We acknowledge the contribution of various members of the Kenwalt SysCAD team in the paper reviews.

REFERENCES

1. Bröcker S, et al. (2021) “Process Simulation – Fit for the Future?” DECHEMA & ProcessNet

2. Ghorbani Y, et al. (2020) “Repurposing Legacy Metallurgical Data Part I: A Move Toward Dry Laboratories and Data Bank” Minerals Engineering