Revolutionising refining with digital twins
Exploring applications and outputs across the refinery landscape
Michelle Wicmandy, Jagadesh Donepudi and Rodolfo Tellez-Schmill KBC (A Yokogawa Company)
R efiners are at the crossroads of innovation and chal - lenge. They are facing disruptions ranging from oil price volatility to the complexities of the global energy transition. Adding to this complexity, Offshore Technology claims India is expanding its pipeline network to more than 29,600 km by 2025. This significant expansion is roughly three-quarters of the Earth’s circumference. As the industry confronts these uncertainties, securing the integrity of this expanding pipeline infrastructure becomes crucial for meet - ing the nation’s growing energy demands while reducing risks and accidents that can harm people, profits, and property.1 Navigating new refining challenges To navigate these new challenges, the refining industry is revamping how it produces, uses, and manages energy.² Although existing assets have data acquisition capabilities, the hurdle lies in reviewing, cleaning, and assessing this data. This process is necessary to determine both current and future operating conditions, as well as meet safety and environmental regulations. In this journey, Indian refiners are in the process of imple - menting digital twins across various refinery process units for long-term sustainability.3 This initiative centres around creating digital twins for diverse applications, which pro - vides real-time visualisation of key performance indicators (KPIs) and benchmark parameters. By using digital twins, refiners can improve the plant’s efficiency and productivity while reducing miscommunication, data waste, and labour costs. Essentially, digital twin technology is revolutionising the way refiners operate and paving the way to long-term profitability.4 Furthermore, it is evident that all aspects of an entire refin - ing supply chain are highly interrelated and complex. Thus, integrating digital twins into the supply chain delivers added value, too, by optimising processes, energy consumption, and control applications such as real-time optimisation (RTO) and advanced process control (APC) systems. In the sup - ply chain, these applications help bridge the gap between forecasting and actual operations. 4 Validating these gaps, or delta vectors, uncovers the disparities between the planned and actual operations in terms of demand, inventory, and production. By validating these delta vectors, supply chain managers can quickly assess and address gaps in their mod - els and processes to accommodate changes in inputs and outputs.1
In regard to process optimisation, which is integrated with supply-side optimisation for power, steam, and utility bal - ances, energy demand takes centre stage. The comparison between linear programming (LP), actual data, and simula - tion enables automated vector updates and model recalibra - tions via artificial intelligence (AI) and machine learning (ML) methods. The following discussions explore various applications, including KPI visualisation, production accounting, LP model updates, process optimisation, real-time optimisation, and corrosion monitoring. The digital twin architecture includes connecting process models through open platform commu - nications unified architecture (OPC UA) with historians to ensure proper calibration. Digital twin technology Digital twins offer a solution to transform the oil and gas industry by improving efficiency and reducing risk. According to researchers,5 these virtual models of physical assets seamlessly connect with real-time data across assets, col - umns, reactors, pipinmg, and equipment. Despite changes in crude quality, catalyst composition, and process conditions, digital twins continuously analyse industrial data to predict and optimise processes.4 Their perpetual operation brings multiple benefits, such as asset monitoring in planning and scheduling studies, refinery-wide flow sheeting, real-time optimisation, and more. Furthermore, digital twins set benchmarks for both the quantity and quality of units. These benchmarks are then transmitted to the RTO/APC layer for optimisation on a global scale.6 This iterative process involves ongoing valida - tion and adjustments to maximise benefits derived through the APC in a closed loop. The APC, armed with its dynamic process model, aims to stabilise operations and reduce fluc - tuations. It effectively implements the desired setpoint from the RTO to achieve closed-loop optimisation.5 The optimiser identifies the optimal operational state and communicates it to the APC. Implementing a digital twin starts with identifying possi - bilities and choosing a pilot configuration with the highest ROI. After implementation, the digital twin becomes an inte - gral part of the enterprise’s digital backbone.4 The final step involves monitoring the value created and modifying the dig - ital twin to maximise economic benefits. Refiners apply digital twins in various applications to
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