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BUSINESS SEGMENTS
Remote Monitoring
Naphtha Cracker optimization
Naphtha Blend optimization
Plant KPI Reporting
Grade sequence optimization
Supply Chain spares optimization
Remote Monitoring
Remote Monitoring
The operations of petrochemical and refinery plants are highly sophisticated. For operators, manually monitoring and operating thousands of equipment, processes, and components is next to impossible. TCG Digital’s remote monitoring solution allows analytics-driven remote supervisory control of plant operations. It is an interactive, real-time plant information system for all plant processes— feedstock, cracking, separation, production, shipping, etc. It gathers real-time data from numerous tags, IoT devices, supply chain, quality, historian, and laboratory systems to provide a centralized operations platform that helps operators visually monitor production levels and operating conditions, and identify problem areas. Close integration with SCADA systems allows operators to remotely change and control various equipment. Also, the dashboards, reports, and alerts enable interactive focused monitoring based on data insights and quick identification of issues helping avoid costly downtime.
Naphtha Cracker optimization
Naphtha Cracker optimization
Naphtha is a commonly used raw material for the production of petrochemicals. Day-to-day production and operational decisions are taken manually resulting in sub-optimal yield. Our naphtha cracker optimization solution maximizes the profit margin of end products such as ethylene, propylene, propane, etc. enabling companies to unlock millions of dollars in value. Machine learning and advanced analytics models are used to predict the product output for any given set of operating parameters, feed quality, and input flows. Then the optimizer predicts the optimal operating parameters, input feed volumes, and desired product mix so that profits are maximized. Consecutively, prices of products and inputs are entered into the optimizer model and operating ranges are set as constraints.
Naphtha Blend optimization
Naphtha Blend optimization
Naphtha feedstock for petrochemicals is procured from various national and international sources, which often come with varying quality of paraffin, olefins, and aromatics. Different quality naphtha inputs are blended using a manual iterative process that is very time-consuming and often does not yield the optimal outputs. Our solution first predicts real-time quality of blended naphtha using machine learning and statistical algorithms. Then it predicts the production yield for a given blending ratio, blended quality, and operating parameters. Finally, it recommends the selection of blending tanks, blending ratio, and operating parameters to maximize yield.
Plant KPI Reporting
Plant KPI Reporting
Plant KPI reporting solution provides intuitive reporting on operations of a plant for tracking day-to-day processes and management. Key metrics and KPIs are tracked across various areas — production, quality, plant utilization, supply chain and financial, etc. Raw data is ingested from equipment, IOT devices, tanks, historian, supply system and quality system, and data insights are generated using dashboards, reports, and alerts. It enables planned versus actual production analysis, production planning, quality monitoring, trend analysis, downtime analysis, and regulatory/emissions reporting. It helps in uncovering hidden insights to reduce refinery losses, yield and waste accounting, yield optimization by predicting optimal plant settings, and identifying interdependencies and sensitivities within and across processes.
Grade sequence optimization
Grade sequence optimization
In a petrochemical production plant, different grades of each product type (ethylene, propylene, etc) are produced for different consumers. Switching the plant operations from one grade to another could prove time-consuming and lead to input wastage. Our grade sequence solution, with the use of advanced analytics and statistical algorithms, recommends the sequence and amount of production for various grades minimizing losses such as material wastage, switching costs, and quality defects, for a given set of operating parameters and work shifts. Historical operation data including defect log and incidents are used for training the model.
Supply Chain spares optimization
Supply Chain spares optimization
This solution drives efficiencies in the supply chain and recommends optimal spares stocking at a central warehouse and plant units, aiming to reduce holding cost without impacting production levels. It has a stocking and order recommendation engine that forecasts spare part consumption using an ensemble predictive modelling approach for each spare part. Business benefits include improved process reliability and order fulfilment rates, optimized inventory costs, reduced spare stock-outs, and better operational planning.