A Dutch SME is embarking on the development of an innovative digital twin software solution aimed at the manufacturing industry. The goal is to create a tool that can calculate, monitor, and predict carbon emissions throughout manufacturing processes. This software will leverage artificial intelligence and advanced modeling techniques to provide real-time analysis and forecasting of carbon footprints.

The SME recognizes the complexity of accurately modeling carbon emissions within manufacturing environments, which involve numerous variables and dynamic processes. Therefore, they are seeking collaboration with R and D institutions or universities that possess expertise in carbon emission calculation methodologies, environmental modeling, and sustainability practices.

The collaboration aims to achieve the following objectives:
- Develop Robust Carbon Emission Models: Create accurate calculation methodologies suitable for integration into the digital twin software, considering all aspects of manufacturing processes, including energy consumption, material usage, and waste generation.
- Integrate Domain Expertise into AI Algorithms: Enhance the predictive capabilities of the solution by incorporating domain-specific knowledge and physics-informed neural networks into the AI models.
- Validate and Calibrate Models: Use real-world data to ensure the reliability and accuracy of the digital twin models, enabling precise monitoring and forecasting of carbon emissions.
- Promote Sustainability Practices: Provide manufacturers with actionable insights to reduce their carbon footprints, optimize processes, and comply with environmental regulations.
- Secure Funding Opportunities: Explore and apply for grants or local funding to support the research and development efforts, leveraging the partner s experience in funding applications.

The SME is particularly interested in domain experts who can contribute specialized knowledge in carbon accounting, life cycle assessment (LCA), and environmental impact analysis within manufacturing settings. The envisioned software will not only measure current emissions but also predict future emissions under various scenarios, supporting manufacturers in their sustainability initiatives and decision-making processes.
Advantages and innovation
The proposed digital twin solution represents an innovative approach to sustainability in the manufacturing industry. By integrating advanced AI models with real-time data from manufacturing processes, the software can accurately calculate, monitor, and predict carbon emissions. The use of physics-informed neural networks allows the incorporation of physical laws and domain-specific knowledge into the AI algorithms, enhancing the accuracy and reliability of predictions.

This solution provides several advantages:
- Real-Time Monitoring and Prediction: Enables manufacturers to track their carbon emissions in real-time and predict future emissions under various operational scenarios.
- Process Optimization: Identifies areas where carbon emissions can be reduced, leading to more efficient and sustainable manufacturing processes.
- Regulatory Compliance: Assists companies in meeting environmental regulations by providing accurate reporting and compliance tools.
- Cost Savings: By optimizing processes and reducing emissions, companies can achieve cost savings through improved efficiency and potential incentives for lower carbon footprints.
- Competitive Advantage: Demonstrates a commitment to sustainability, enhancing the company s reputation and appeal to environmentally conscious customers and stakeholders.

The innovation lies in the combination of digital twin technology with advanced AI models enhanced by domain expertise in carbon emissions. This integrated approach allows for a more precise and actionable understanding of environmental impacts in manufacturing.
Technical Specification or Expertise Sought
The SME seeks R and D institutions, universities, or research organizations with expertise in carbon emission calculation methodologies and environmental modeling within manufacturing settings. The ideal partner should have extensive knowledge in calculating carbon emissions specific to manufacturing processes, including both direct and indirect emissions.

They should possess experience in developing customized carbon footprint models that can be integrated into digital twin and AI solutions. Familiarity with methods such as physics-informed neural networks and the ability to embed domain-specific knowledge into AI algorithms are essential.

The partner should be capable of working closely with the SME s development team, providing technical input on modeling, algorithm design, and system integration. They should assist in validating and calibrating the models using real-world data to ensure accuracy and reliability.

In terms of collaboration, the SME is looking for a research cooperation agreement. They are interested in partners who can contribute expertise rather than products or services for sale. Offers that focus solely on generic carbon accounting software without customization or integration capabilities would not be suitable.

As for financial aspects, the SME anticipates a collaborative research arrangement and is open to discussing cost-sharing models or joint funding applications. Specific prices or quantities are not applicable, as the focus is on expertise and collaboration rather than procurement of goods.