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The Artificial Neural Twin - Process optimization and continual learning in distributed process chains.

Johannes Emmert1, Ronald Mendez1, Houman Mirzaalian Dastjerdi1

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Summary
This summary is machine-generated.

This study introduces the Artificial Neural Twin, a novel approach for industrial process optimization. It enhances economic and ecologic efficiency by integrating AI, sensor networks, and control strategies for better data management and model adaptability.

Keywords:
Continual learningData-fusionDecentralized and distributed controlDistributed learningInternet of thingsModel predictive controlMulti sensor systemsProcess optimization

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Area of Science:

  • Industrial Process Engineering
  • Artificial Intelligence
  • Control Systems

Background:

  • Holistic industrial process optimization faces challenges due to data sovereignty, diverse objectives, and expert knowledge requirements.
  • Data-driven AI methods in industrial settings often need frequent recalibration to address distribution drifts.
  • Current methods struggle with decentralized data fusion and adaptable process control.

Purpose of the Study:

  • To propose a novel framework, the Artificial Neural Twin, for overcoming limitations in industrial process optimization and control.
  • To enable decentralized, differentiable data fusion for state estimation in distributed process steps.
  • To facilitate process optimization and AI model fine-tuning through gradient backpropagation.

Main Methods:

  • Combining concepts from Model Predictive Control, Deep Learning, and Sensor Networks.
  • Implementing decentralized, differentiable data fusion for state estimation.
  • Utilizing a quasi-neural network structure to backpropagate loss gradients.
  • Demonstration on a simulated Unity-based virtual machine park for plastic recycling.

Main Results:

  • The Artificial Neural Twin effectively integrates distributed process steps.
  • The approach allows for gradient-based optimization of process parameters and AI models.
  • Successful demonstration in a simulated environment highlights practical applicability.
  • Improved economic and ecologic efficiency in industrial processes is achievable.

Conclusions:

  • The Artificial Neural Twin offers a robust solution for complex industrial process optimization.
  • This framework enhances adaptability to distribution drifts and addresses data sovereignty concerns.
  • The method provides a pathway for more efficient and sustainable industrial operations.