Adaptive thermal cascade optimization for real-time heat integration
A hybrid process model that combines thermodynamic principles with machine learning to optimize cross-stage heat integration in real-time, offering 8-15% energy savings in refining operations.
Overview
The adaptive thermal cascade optimization system revolutionizes heat integration in continuous refining operations by employing hybrid models that synergize thermodynamic constraints with machine learning. This innovative solution dynamically recalculates optimal heat recovery configurations in real-time, addressing variations in operating conditions such as throughput and ambient factors. By predicting the minimum thermal severity needed for deodorization while maintaining quality, the system enables significant energy savings, estimated between 8-15%. Additionally, it identifies windows for utilizing waste heat from deodorization to preheat bleaching processes without compromising process stability.
Technical specifications
Key features:
- Dynamic pinch optimization: Continuously updates heat recovery configurations to adapt to real-time operating conditions.
- Quality-constrained energy reduction: Predicts and applies the minimum thermal severity required to maintain product specifications, reducing energy usage.
- Cascade opportunity identification: Uses machine learning to exploit transient opportunities for waste heat utilization.
- Hybrid modeling approach: Combines first principles of thermodynamics with machine learning to learn plant-specific behaviors and optimize operations.
Applications:
- Suitable for large-scale refining operations with significant temperature differentials and continuous operations.
- Ideal for plants aiming to improve energy efficiency and reduce operational costs in deodorization and bleaching processes.
Technology readiness level
The technology has reached a Technology Readiness Level (TRL) of 4. It is currently undergoing pilot deployment to further validate its effectiveness in real-world scenarios. Initial results from pilot deployments will focus on measuring energy savings and quality consistency to assess return on investment.
About intemic
Intemic is an operational intelligence platform designed for industrial engineering and operations teams. The platform functions by unifying fragmented data from ERP, MES, LIMS, SCADA, and IoT systems into a single, canonical knowledge base. By layering AI agents, automations, and low-code dataflows on top of this unified data, the company enables real-time monitoring, process simulation, and predictive analytics. Unlike traditional, highly customized industrial software, Intemic provides a scalable environment that allows users to map entities and regulatory contexts automatically, significantly reducing deployment timelines for applications such as predictive maintenance, quality assurance, and automated audit reporting.
This technology is critical for manufacturing, energy, and life sciences companies aiming to improve operational efficiency, sustainability, and regulatory compliance. By replacing manual, disconnected processes with traceable, AI-driven workflows, Intemic helps organizations reduce production waste, minimize unplanned downtime, and streamline complex reporting requirements like scope 3 emissions calculations. The platform supports both cloud SaaS and on-premise deployments, offering enterprise-grade security controls such as SOC 2 compliance and role-based access management to ensure that complex industrial processes remain auditable, intelligent, and sustainable by default.
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