Industrial AI 

Best Industrial AI for predictive AI solution and optimize process performance to achieve your business and sustainability targets

Improved predictability, optimal product quality and use of resources through Industrial Iot and AI

Utilization of historial process data and usage of IoT sensors in capturing industrial process status are corner stones for AI based optimization. By utilizing deep learning models in predicting and simulating the process performance in the future, the right operative actions can be made to achieve set business goals with high certainty.

Industrial AI-From history and real-time data to future predictions and optimal process performance

Top Data Science Industrial AI solution concept and approach enables IoT and AI based process optimization to be deployed to a wide range of industries including Chemicals, Pharmaceuticals, Biotechnology and Food & Beverage. In each case the industrial process is divided into stages for which the process parameters, sensor data and adequate ML models are used for optimization. The optimized parameters can vary from chemicals, to temperature, pressure, energy usage just to name a few. An easy-to-use end-user application can be included in the solution or it can be used as part of another system as a service.

Industrial AI for Biomaterials companies

The ultimate goal of Industrial Process Control is to improve the plant’s performance in terms of uptime, quality and production. As a well-established technology for advanced process control (APC) in many industrial applications, model predictive control (MPC), to some extent, has been able to control multivariable processes with complex constraints, with delays and with strong interactive feed-forward and feedback loops.

Artificial Intelligence, especially deep learning neural networks (DL), can upgrade MPC from the traditional rule-based to more data driven mechanisms of controlling, which is the solution to the configuration challenges of traditional MPC implementation. Top Data Science brings world-class AI expertises, our best practices and our state-of-the-art tools to help industrial companies to be successful with AI for process optimization.

Industrial Process Control, Deep Neural Networks

The ultimate goal of industrial process control is to improve the plant’s performance in terms of uptime, quality and production. As a well-established technology for advanced process control (APC) in many industrial applications, model predictive control (MPC), to some extent, has been able to control multivariable processes with complex constraints, with delays and with strong interactive feed-forward and feedback loops. Artificial Intelligence, especially deep learning neural networks (DL), can upgrade MPC from the traditional rule-based to more data driven mechanisms of controlling, which is the solution to the configuration challenges of traditional MPC implementation. Top Data Science brings world-class AI expertises, our best practices and our state-of-the-art tools to help industrial companies to be successful with AI for process optimization.

Benefits

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Improved profitability through optimized production and quality

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Improved sustainability of the operations

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Improved predictability, transparency and process control

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Higher customer satisfaction

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Ability to utilize best practices from other industries

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Proven ability to deliver robust and scalable AI solutions

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Artificial Intelligence for IoT and Process Optimization

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