Refining India 2022 Newspaper

refining india 2022

Digitalisation on cloud Most refineries are adopting cloud-ena- bled digitalisation to reap the benefits of technology in terms of scale-up, security, and availability of the applications and ser- vices to compress the decision-making cycle and empowerment of employees. Traditionally all these applications are used in silos, and the data sources are dif- ferent; hence the interpretations are lim- ited to specific tasks without a holistic approach. Jagadesh Donepudi, Ashok Pathak and Mike Aylott KBC (A Yokogawa Company), Mumbai Dashboard (waylay) Control network Sushi sensor Lora MQTT Lora gateway On boarding Visuali s ation API layer Ingress

Custom UI for applications

API monitoring and maintenance


Trac management




Management monitoring


Visual logic builder/runtime

Device management

Data Aggregator


With the advent of the Internet of Things, obtaining data from remote loca- tions became easy by deploying sensors and wireless technologies. For refineries today, the data is centralised in the distrib- uted control system (DCS) control rooms, and the same data is being used for con- trols, automation, and analytics. Industrial Internet of Things (IIoT) sensors allow refiners to add to the conventional data with new information, enabling improved pictures of equipment health. With the application of linear models, like linear programming (LP), the supply chain cycle demand forecast, planning and scheduling have made it possible to pro- cure the right crudes, process them in a given refinery configuration, and meet the demand scenarios. Many times at the end of the month after backcasting, we observe gaps between plan and actual. The yields and qualities do not match with the actuals. The differ- ences may be due to feed qualities, meas- urements, catalyst performance, and so on. Process simulation tools, such as Petro-SIM, help analyse these deviations, accounting for the non-linearities, mass balances, and measurements. One of the solutions KBC is architecting is digital twins of the entire plant, including the major equipment, connected with real- time data and running the applications on a cloud-based platform such Yokogawa Cloud, hosted on a commercial/private cloud platform like Azure or AWS. The engineering applications such as process simulation, in our case Petro- SIM and Visual MESA Energy Real Time Optimisation, reside in the containers where the data is received. The plant and calculations are carried out in the cloud, and the results are published as a key per- formance indicator (KPI) on the visualisa- tion layer through a browser. Digitalisation on Cloud KBC’s digitalisation architecture is both flexible and agnostic of the cloud platform selected. In collaboration with Yokogawa, we are redeveloping our applications to work on Yokogawa’s standard cloud plat- form (see Figure 1 ), as well as enabling the component parts to plug into other cloud platforms such as Microsoft Azure or AWS using industry-standard containerisation and integration techniques: • Standard Docker images for engines such as Petro-SIM • REST application programming inter- face (APIs), enabling largely automatic integration with platform components such as asset models, time series data-


IOT hub*

Resource meta data


API management

Container engine K8s

Time series data

Rest API



API platform monitoring

Data transformations



Relational data


Application services


Figure 1 Cloud architecture with Yokogawa platform

vals, while others will have to respond to triggers based on new data availability and other events. The big picture of digital twins inte- grated with planning, scheduling, and recover time objective (RTO) on Lookahead mode enables obtaining the right set points to the APCs. Lookback mode pro- Did you know KBC’s digitalisation architecture is both flexible and agnostic of the cloud platform selected?

bases, and relational datastores and ena- bling flexible data import and export • Web-based user interfaces (UI) This approach allows considerable flex- ibility in architecting solutions. Taking Petro-SIM process digital twins as an example, they can be implemented: • On-premise, tying into site historian sys- tems like IP-21 and LIMS • Natively on a digital platform such as Azure tenant as Docker images that work with Petro-SIM models built on the desk- top today and with fully web-based Petro- SIM in the future • Natively on the Yokogawa platform deployed on Azure tenant and integrated with your systems of record • Natively on the Yokogawa platform as software as a service (SaaS) application Digital Twins Architectures Refinery planning, scheduling, and con- trol operations involve multiple models that need maintenance and, too often, existing processes are highly manual. Implementing digital twins enables run- ning these applications automatically on digital platforms, as shown in Figure 2 . Some digital twin calculations can run on fixed timers, such as hourly or daily inter-

models through recalibration of the mod- els as required.

Digital Twin Workflows The above digital twin applications are developed using KBC’s proprietary Petro-SIM flowsheet embedded with SIM reactor models. These digital twins are linked to data historians allowing them to gather and process data from the refinery data historian, enabling unit monitoring, visualisation, what-if scenarios, and other automation of the model upkeep. Digital twins perform data reconcili- ation and calculate KPIs on a process unit, such as feed quality summaries, unit yields, process conditions, and reactor severities (such as conversion) for anal- ysis by process engineers, and also the economic value of the streams with all the prices in place. A digital twin will perform the follow- ing key tasks using a standard automated methodology: • Retrieve and screen process and lab data • Reconcile unit material balances • Calculate key, unmeasured process variables • Calculate a set of KPIs • Provide the ability to trend actual ver- sus simulation versus plan • Summarise deviation of trends in the status report of KBC Explorer • Generate a set of reports – material balance (raw and reconciled), data quality indicators, and unit KPIs • Integration of digital twins with artifi- cial intelligence/machine learning (AI/ML) to provide auto-calibration of the models and updating of the LP vectors Use Cases The Petro-SIM Explorer has been used for graphical trending of actual versus simulation versus LP sub-model values for given process variables, such as unit operation variables, yields, and prod- uct properties. The SIM model predic- tions are tracked and compared against

vides reconciled data by comparing actual results with calculated parameters using data reconciliation and correction to get the right data to simulation models and updates of LP vectors in the planning

Production schedule

Crude schedule

Energy schedule

Energy demands

Digital twin – CDU Lookahead

Daily scheduling

Daily energy scheduling

Production plan

Baseline data

ERTO targets

RTO targets

Daily production accounting

Process RTO

Energy RTO

Plant data

Process data

Historian data

Utilities system APC/DCS

Plant data


OMS data, LIMS data


Digital twin – CDU


Crude unit



Lookback 24

Plant performance KPIs

Figure 2 Digital twin architecture


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