HTRI SmartPM^™

As an advanced digital platform, HTRI SmartPM is engineered to facilitate digital transformation through advanced monitoring, predictive analytics, and proactive maintenance of industrial thermal systems (including digital twins of heat exchangers and fired heaters). It integrates decades of HTRI's research in heat transfer, thermodynamics, and process control with the latest in physics-based modeling and artificial intelligence technologies.

SmartPM is utilized by plant personnel, maintenance engineers, procurement and R&D departments, engineering teams, energy efficiency analysts, and heat transfer specialists. They use it for a range of tasks, including

• managing cleaning schedules
• optimizing heat recovery
• analyzing retrofit and revamp options
• assisting in crude procurement decisions
• assessing the effects of fuel switches
• reducing both fuel consumption and emissions

Direct bi-directional integration with data historians allows for effortless handling of vast and varied industrial data. SmartPM utilizes proprietary data reconciliation processes to streamline the monitoring of heat exchanger performance, reducing the need for extensive user input. Additionally, its predictive analytics are reinforced by validations from an active user community. The digital transformation of data management enables users to make informed decisions based on the complex data.

The setup of SmartPM involves constructing digital twin models for heat exchangers, using Xist^® and Xfh^® Ultra designs. This process integrates detailed heat exchanger design data into the SmartPM system, ensuring accurate digital twin heat exchanger modeling and data integration.

SmartPM's digital twin models for heat exchangers and fired heaters deliver a detailed understanding of system performance. These models are crucial for examining historical performance, capturing the effects of events like specific cleaning actions or heat exchanger modifications, and tracking operational changes over time. The ability to keep a record of historical events and consequences is invaluable for future forecasting and decision-making.

The digital twin models offer deep insights into fouling trends, cleaning schedules, and performance patterns, equipping users with the foresight needed for proactive system management. Furthermore, they allow users to assess how different economic, environmental, and technical conditions affect key performance indicators such as energy efficiency, emissions, production output, and safety. This holistic approach to monitoring and forecasting enables well-informed decisions, promoting efficient resource use and maintaining high safety standards.

Digital twin models play a key role in predicting and mitigating fouling in heat exchangers, effectively identifying operational inefficiencies and preventing potential issues. These models are crucial in managing crude oil fouling by providing essential insights for both maintenance and operational strategies. They excel in distinguishing between efficient and less efficient practices, leveraging past performance data to offer actionable intelligence. These models enhance proactive decision-making by alerting users to inefficiencies and approaching limits. Furthermore, their capabilities in visualizing scenarios and assessing 'what if' situations enable a more innovative and data-driven approach to heat exchanger system management.

SmartPM optimizes heat exchanger network performances by offering ideal flow splits for reduced fouling and enhanced energy recovery. It also facilitates effective maintenance planning and scheduling, aiding in fouling mitigation and troubleshooting. This approach not only improves efficiency but also lessens environmental impact.

SmartPM tackles the issue of fouling in heat exchangers (and their networks) and fired heaters, a significant challenge that leads to increased operating costs, environmental impact, and safety hazards.

This feature allows SmartPM to estimate missing measurements, reduce data uncertainty, and identify sensor issues, providing trusted information for insightful performance predictions.

By warning operators of potential throughput limitations and optimizing heat transfer equipment, SmartPM helps avoid production losses, reduce fuel consumption, and minimize emissions.

SmartPM's feature for predicting crude oil fouling is tailor-made to address fouling concerns in crude oil preheat trains. By utilizing dynamic fouling models that integrate a deep understanding of fouling mechanisms, along with a blend of model training and validation using monitoring data, SmartPM offers accurate insights into fouling patterns. This enables proactive maintenance and operational dynamics, ensuring that preheat train efficiency is optimized and energy waste is minimized.

Crude distillation units are energy-intensive and benefit from the efficient operation of the crude preheat train and fired heaters. SmartPM and Xfh Ultra model these components as digital twins, providing a dynamic evaluation platform. This platform assesses the effects of operational changes, including the fouling in both the crude preheat train and fired heaters, variations in fuel composition, and their implications on safety and emissions. By capturing these dynamics, the models play a crucial role in optimizing the operational efficiency of the entire plant.

SmartPM has the proven ability to model hydrocarbon fouling across a range of systems, including heat exchanger networks associated with units like crude distillation units (CDU), vacuum distillation units (VDU), hydrodesulfurization systems (hydrotreaters, HDS), coker feeders, visbreakers, hydrocrackers, fluid catalytic crackers (FCC), and condensate splitters. The platform's dynamic and statistical fouling models, backed by a track record of success, utilize advanced prediction algorithms. These algorithms empower users to anticipate and proactively address fouling issues in their early stages. This proactive approach is key to maintaining seamless and efficient operations in equipment processing hydrocarbons, thus avoiding downtime and inefficiencies.

The SmartPM platform is a versatile digital transformation tool designed for thermal systems. It utilizes Xist for modeling heat exchangers and Xfh Ultra for fired heaters (along with their associated networks). SmartPM is effectively employed in industries such as crude oil refineries, petrochemicals, and the food sector. In these fields, the platform leverages digital twin models of heat exchangers and fired heaters, integrating them with plant data for comprehensive performance monitoring. This includes advanced capabilities in fouling monitoring, prediction, and troubleshooting.

SmartPM has been innovatively applied to manage critical temperatures, analyze the impact of past and present performance, and recommend optimal future strategies. Its digital twin technology provides a detailed view of system behavior, enabling informed decision-making for enhanced operational efficiency.