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Digitalization and Data Analytics for Waste Heat Recovery

Detailed overview of innovation with sample startups and prominent university research

What it is

Digitalization and data analytics in industrial waste heat recovery involve using digital technologies and data analysis techniques to optimize the performance and efficiency of waste heat recovery systems. This approach leverages data-driven insights to improve system design, operation, and maintenance, leading to increased energy recovery and reduced costs.

Impact on climate action

Digitalization and Data Analytics in Industrial Waste Heat Recovery optimize energy usage and enhance efficiency. By identifying heat recovery opportunities and optimizing processes, this innovation reduces energy consumption, lowers greenhouse gas emissions, and promotes sustainability, contributing to climate action by maximizing resource utilization and minimizing waste in industrial operations.

Underlying
Technology

Sensors and Data Acquisition: Waste heat recovery systems are equipped with sensors that collect data on various parameters, such as temperature, pressure, flow rate, and energy output. This data is essential for monitoring and optimizing system performance.
Data Analytics and AI: Data analytics and AI algorithms are used to analyze waste heat recovery system data, identify performance trends, detect potential issues, and provide optimization recommendations.
Cloud-Based Platforms: Cloud-based platforms provide the infrastructure and computing power needed to store, analyze, and visualize large amounts of waste heat recovery system data.
Digital Twins: Digital twins are virtual representations of physical waste heat recovery systems that can be used to simulate and optimize system performance in real-time.
Remote Monitoring and Control: Digitalization enables remote monitoring and control of waste heat recovery systems, allowing operators to adjust system parameters and troubleshoot issues from anywhere.

TRL : 7-8

Prominent Innovation themes

AI-Powered System Optimization: Advanced AI algorithms and machine learning techniques are being developed to optimize waste heat recovery system performance in real-time, taking into account factors such as waste heat source characteristics, energy demand, and environmental conditions.
Predictive Maintenance: AI and data analytics can be used to predict potential equipment failures in waste heat recovery systems, allowing for proactive maintenance and reducing downtime.
Automated Fault Detection and Diagnostics: Smart monitoring systems can automatically detect and diagnose faults in waste heat recovery systems, enabling faster troubleshooting and repairs.
Digital Twin-Based Optimization: Digital twins of waste heat recovery systems can be used to simulate and optimize system design, operation, and maintenance strategies.
Integration with Industrial Process Control Systems: Waste heat recovery systems can be integrated with industrial process control systems to optimize energy flows and maximize the utilization of recovered heat.

Other Innovation Subthemes

Real-time Performance Monitoring
Energy Optimization Algorithms
Proactive Equipment Maintenance
Automated Fault Detection
Virtual Simulation Platforms
Cloud-Based Data Analysis
Remote System Control
Integration with Process Control
Sensor Technology Advancements
Predictive Analytics Solutions
Machine Learning Applications
Digital Twin Technology

Related Innovations

Sample Global Startups and Companies

SenseOps:
- Technology Enhancement: SenseOps offers digitalization and data analytics solutions for industrial and commercial facilities. Their platform collects real-time data from sensors and equipment, providing insights into energy usage, operational efficiency, and equipment health. SenseOps employs advanced analytics and machine learning algorithms to optimize processes, reduce energy consumption, and prevent equipment failures.
- Uniqueness of the Startup: SenseOps stands out for its focus on providing actionable insights and predictive analytics for energy-intensive industries and facilities. Their platform enables data-driven decision-making, proactive maintenance, and continuous improvement, helping customers optimize resource utilization and achieve sustainability goals.
- End-User Segments Addressing: SenseOps serves industries such as manufacturing, healthcare, hospitality, and commercial real estate seeking to enhance operational efficiency, reduce costs, and minimize environmental impact. Their digitalization and data analytics solutions are deployed in facilities looking to leverage data insights for performance optimization and asset management.
Everactive:
- Technology Enhancement: Everactive specializes in batteryless, wireless sensors and IoT (Internet of Things) platforms for industrial and commercial applications. Their self-powered sensors collect and transmit data on various parameters such as temperature, vibration, and humidity, enabling continuous monitoring and predictive maintenance. Everactive’s technology eliminates the need for battery replacements, offering long-term, maintenance-free operation.
- Uniqueness of the Startup: Everactive stands out for its innovative approach to IoT sensor technology, leveraging energy harvesting techniques to power sensors indefinitely without batteries. Their solution enables pervasive sensing and data collection in environments where traditional wired or battery-powered sensors are impractical or cost-prohibitive.
- End-User Segments Addressing: Everactive serves industries such as manufacturing, oil and gas, utilities, and building automation, where continuous monitoring and predictive maintenance are critical for operational efficiency and safety. Their batteryless sensor platform is deployed in industrial processes, equipment monitoring, and environmental sensing applications.
Atonometrics:
- Technology Enhancement: Atonometrics develops advanced testing and monitoring solutions for photovoltaic (PV) systems and solar energy projects. Their platform enables comprehensive performance testing, monitoring, and data analytics for solar panels, inverters, and balance of system components. Atonometrics’ technology helps optimize solar energy production, identify system inefficiencies, and ensure reliability and safety.
- Uniqueness of the Startup: Atonometrics stands out for its expertise in solar PV testing and monitoring, offering scalable solutions for research, development, and commercial deployment of solar energy technologies. Their platform provides real-time data insights and diagnostics, enabling stakeholders to maximize energy yield and ROI (Return on Investment) for solar projects.
- End-User Segments Addressing: Atonometrics serves solar project developers, EPC (Engineering, Procurement, and Construction) contractors, PV module manufacturers, and research institutions involved in solar energy deployment and research. Their testing and monitoring solutions are used in utility-scale solar farms, distributed generation projects, and solar research facilities.

Sample Research At Top-Tier Universities

National Renewable Energy Laboratory (NREL):
- Research Focus: NREL is a leading research institution in the field of renewable energy, including industrial waste heat recovery. Their focus on Digitalization and Data Analytics involves developing advanced sensor networks, data acquisition systems, and machine learning algorithms to monitor, analyze, and optimize industrial processes for waste heat recovery.
- Uniqueness: NREL’s research leverages real-time data analytics and predictive modeling techniques to identify waste heat sources, quantify heat losses, and assess the feasibility of heat recovery technologies in various industrial sectors. They also collaborate with industry partners to develop customized digital solutions for optimizing energy efficiency and reducing greenhouse gas emissions.
- End-use Applications: The outcomes of their work have applications in diverse industries such as manufacturing, chemical processing, and power generation. By harnessing digitalization and data analytics, NREL’s research enables industrial facilities to improve energy productivity, enhance process efficiency, and achieve cost savings through effective waste heat recovery strategies.
Technical University of Munich (TUM):
- Research Focus: TUM conducts innovative research on Digitalization and Data Analytics for Industrial Waste Heat Recovery, leveraging its expertise in process engineering, thermodynamics, and computational modeling. Their research aims to develop smart sensing technologies, data-driven optimization methods, and virtual simulation tools for enhancing the performance and reliability of waste heat recovery systems.
- Uniqueness: TUM’s research integrates advanced sensors, IoT devices, and cloud-based platforms to collect, process, and analyze large volumes of data from industrial heat sources. They also develop physics-based models and control algorithms to optimize heat exchanger design, fluid dynamics, and thermal management strategies for waste heat utilization.
- End-use Applications: The outcomes of their work find applications in industrial plants, district heating networks, and renewable energy systems. By leveraging digitalization and data analytics, TUM’s research enables more efficient use of waste heat resources, reduces energy consumption, and contributes to the transition to a more sustainable and circular economy.
Massachusetts Institute of Technology (MIT):
- Research Focus: MIT is at the forefront of research on Digitalization and Data Analytics for Industrial Waste Heat Recovery, leveraging its multidisciplinary approach to address complex energy challenges. Their research involves developing advanced sensing technologies, big data analytics, and optimization algorithms to maximize the utilization of waste heat in industrial processes.
- Uniqueness: MIT’s research focuses on integrating digital twins, machine learning algorithms, and real-time control strategies to enable proactive monitoring, fault detection, and adaptive control of waste heat recovery systems. They also explore the integration of renewable energy sources, thermal energy storage, and demand-side management techniques to enhance system flexibility and resilience.
- End-use Applications: The outcomes of their work have applications in manufacturing plants, chemical refineries, and urban energy systems. By harnessing digitalization and data analytics, MIT’s research enables industrial facilities to achieve higher energy efficiency, reduce environmental impact, and enhance their competitiveness in a rapidly evolving market.

Commercial Implementation

Digitalization and data analytics technologies are being increasingly implemented in industrial waste heat recovery systems, particularly in large-scale industrial facilities and energy-intensive industries. These technologies are helping companies improve energy efficiency, reduce costs, and minimize environmental impact.

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