Digitalization and Process Optimization for Biomass power

Detailed overview of innovation with sample startups and prominent university research

What it is

Digitalization and process optimization involve using digital technologies and data analytics to improve the efficiency and effectiveness of industrial processes. This approach can lead to significant reductions in energy and resource consumption, as well as improved product quality and reduced waste.

Impact on climate action

Digitalization and Process Optimization in Biomass for Heating & Power amplify climate action by maximizing energy efficiency and reducing emissions. By optimizing biomass combustion processes through data-driven insights and automation, this innovation minimizes environmental impact, lowers carbon emissions, and advances the transition to sustainable heating and power generation methods.


  • Industrial Internet of Things (IIoT): IIoT sensors and devices collect real-time data on process parameters, such as temperature, pressure, and flow rate. This data is used to monitor and optimize processes.
  • Data Analytics and AI: Data analytics and AI algorithms can be used to analyze process data, identify inefficiencies, and recommend improvements. This enables data-driven decision-making and continuous process optimization.
  • Process Modeling and Simulation: Software tools can be used to model and simulate industrial processes, allowing engineers to test different scenarios and optimize process parameters before implementing changes in the real world.
  • Cloud Computing: Cloud computing platforms provide the infrastructure and computing power needed to store, analyze, and visualize large amounts of process data.
  • Digital Twins: Digital twins are virtual representations of physical assets and processes that can be used to simulate and optimize process parameters in real-time.

TRL : 7-8

Prominent Innovation themes

  • AI-Powered Process Optimization: Advanced AI algorithms and machine learning techniques are being developed to optimize process parameters in real-time, leading to continuous improvement and adaptation to changing conditions.
  • Digital Twins for Process Simulation: Digital twins are becoming more sophisticated and integrated with real-time data, allowing for more accurate and effective process simulation and optimization.
  • Edge Computing for Industrial IoT: Edge computing brings computing power and data analysis closer to the source of data collection, enabling faster and more efficient process optimization.
  • Augmented Reality (AR) and Virtual Reality (VR) for Process Visualization: AR and VR technologies can be used to visualize process data and digital twins, providing engineers with a more intuitive and immersive way to understand and optimize processes.

Other Innovation Subthemes

  • Real-time Monitoring and Control Systems
  • Predictive Maintenance and Fault Detection
  • Waste Reduction and Resource Optimization
  • Seamless Integration of Digital Twins
  • Remote Operation and Management Solutions
  • Data-Driven Decision Support Systems
  • Scalable Cloud Infrastructure for Data Analytics
  • Augmented Reality for Training and Maintenance
  • Sustainable Supply Chain Management
  • Cybersecurity Solutions for Industrial IoT
  • Continuous Process Improvement Frameworks
  • Smart Grid Integration for Power Generation
  • Multi-scale Process Modeling and Simulation
  • Cross-sector Collaboration Platforms

Sample Global Startups and Companies

  • Process Miner:
    • Technology Enhancement: Process Miner offers advanced analytics and process mining solutions that enable companies to gain insights from operational data and optimize their business processes. Their platform utilizes machine learning algorithms and data visualization techniques to analyze event logs, identify process inefficiencies, and recommend improvement opportunities. By leveraging real-time data analytics, Process Miner helps organizations streamline operations, reduce costs, and enhance productivity.
    • Uniqueness of the Startup: Process Miner stands out for its focus on process mining and its ability to uncover hidden patterns and inefficiencies in business processes. Their platform offers end-to-end visibility into process performance, enabling data-driven decision-making and continuous process improvement. With a user-friendly interface and powerful analytics capabilities, Process Miner empowers organizations to drive digital transformation and achieve operational excellence.
    • End-User Segments Addressing: Process Miner serves industries such as manufacturing, utilities, healthcare, and finance, where optimizing operational processes is critical for success. Their solutions are deployed by process engineers, operations managers, and business analysts seeking to enhance efficiency, quality, and compliance across diverse business processes.
  • Seeq:
    • Technology Enhancement: Seeq provides advanced analytics software for process manufacturing industries, allowing users to visualize, analyze, and optimize operational data. Their platform integrates with existing data systems and historian databases to perform complex data analysis, identify trends, and uncover insights. Seeq’s capabilities include predictive analytics, anomaly detection, and root cause analysis, enabling users to improve process performance and decision-making.
    • Uniqueness of the Startup: Seeq stands out for its focus on process analytics and its user-friendly interface designed for engineers and industrial professionals. Their platform offers intuitive data visualization and collaboration tools, making it easy for users to explore data, generate insights, and share findings across teams. Seeq’s agile approach to analytics enables faster time-to-value and empowers organizations to drive continuous improvement in their operations.
    • End-User Segments Addressing: Seeq serves process manufacturing industries such as oil and gas, chemicals, pharmaceuticals, and utilities, where optimizing production processes is essential for competitiveness and compliance. Their software is used by engineers, data scientists, and plant operators to analyze data from sensors, equipment, and production systems, improving efficiency, reliability, and safety.
  • TrendMiner:
    • Technology Enhancement: TrendMiner offers self-service analytics software for industrial process data analysis and optimization. Their platform employs advanced analytics techniques such as pattern recognition, correlation analysis, and predictive modeling to uncover insights from time-series data. TrendMiner enables users to identify process deviations, optimize performance, and reduce downtime through proactive monitoring and analysis.
    • Uniqueness of the Startup: TrendMiner stands out for its focus on self-service analytics and its emphasis on empowering subject matter experts to drive process improvements. Their platform provides intuitive tools for data exploration, visualization, and modeling, allowing users to quickly derive actionable insights from complex industrial data. TrendMiner’s collaborative approach to analytics fosters cross-functional collaboration and knowledge sharing within organizations.
    • End-User Segments Addressing: TrendMiner serves process industries such as chemicals, petrochemicals, pharmaceuticals, and food and beverage, where optimizing production processes is critical for operational excellence and regulatory compliance. Their software is used by process engineers, production managers, and reliability engineers to analyze data from process historians, identify optimization opportunities, and drive continuous improvement initiatives.

Sample Research At Top-Tier Universities

  • Massachusetts Institute of Technology (MIT):
    • Research Focus: MIT is a leader in research on Digitalization and Process Optimization for Biomass Heating & Power, focusing on leveraging digital technologies, automation, and advanced control systems to enhance the efficiency, flexibility, and sustainability of biomass energy systems.
    • Uniqueness: Their research involves developing integrated sensor networks, data analytics platforms, and optimization algorithms to monitor and control biomass feedstock quality, combustion processes, and energy conversion systems in real-time. They also explore the integration of biomass energy with renewable resources, energy storage, and grid services to optimize resource utilization and mitigate environmental impacts.
    • End-use Applications: The outcomes of their work have applications in district heating, industrial cogeneration, and decentralized power generation. By digitizing and optimizing biomass energy systems, MIT’s research contributes to improving energy security, reducing emissions, and advancing the transition to a low-carbon energy economy.
  • Stanford University:
    • Research Focus: Stanford University conducts innovative research on Digitalization and Process Optimization for Biomass Heating & Power, leveraging its expertise in data science, optimization theory, and energy systems engineering to develop intelligent solutions for biomass energy management and control.
    • Uniqueness: Their research encompasses the development of advanced modeling frameworks, control strategies, and predictive analytics tools for optimizing biomass supply chains, combustion processes, and heat/power generation systems. They also investigate the integration of machine learning, AI-driven optimization, and cyber-physical systems to enable autonomous operation and adaptive control of biomass energy infrastructure.
    • End-use Applications: The outcomes of their work find applications in industrial facilities, agricultural operations, and municipal energy systems. By advancing digitalization and process optimization for biomass heating & power, Stanford’s research supports the transition to sustainable and resilient energy systems, fostering economic development and environmental stewardship.
  • Carnegie Mellon University (CMU):
    • Research Focus: CMU is engaged in cutting-edge research on Digitalization and Process Optimization for Biomass Heating & Power, leveraging its expertise in systems engineering, human-computer interaction, and energy policy to develop innovative solutions for biomass energy integration and optimization.
    • Uniqueness: Their research involves the design and implementation of digital twins, simulation models, and decision support systems for biomass energy planning, scheduling, and control. They also explore socio-technical aspects, stakeholder engagement, and policy implications of biomass energy deployment to address social, economic, and environmental challenges.
    • End-use Applications: The outcomes of their work have applications in rural electrification, community-based energy projects, and sustainable development initiatives. By promoting digitalization and process optimization for biomass heating & power, CMU’s research supports energy access, poverty alleviation, and climate resilience in diverse socio-economic contexts.

commercial_img Commercial Implementation

Digitalization and process optimization technologies are being implemented by companies across various industries, including manufacturing, chemical processing, and energy. These technologies are helping businesses improve efficiency, reduce costs, and minimize environmental impact.