Artificial Intelligence in Process Engineering

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The purpose of ANACON-AI package is to provide process engineers with set of modern AI tools, which enables connectivity, validation and prediction of main KPIs, which enables to take the correct decisions to maintain and improve effective industrial processes management. The software calculates and predicts physical properties and chemical compositions for different process streams, and proposes required process set points, that will accomplish the calculated predictions. Process analyzers provides online analytic data, which is verified and validated against the laboratory results and predicted products quality.

Using the modern AI tools powered by accurate KPIs measurement, forms a basic tool from managers to operators to take the correct decisions to maintain and improve effective industrial processes management.

The starting point for choosing which KPIs are key to a particular process, should be to be focused on those, that can characterize the given inputs against target outputs. The obvious way for this exercise in downstream industry is to apply KPIs, which are directly related to the quality of process streams, i.

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Additional KPIs to be considered are related to safety, security and environmental requirements, which shall be applied as a constraints. This method enables overall process optimization through integration of the network input and target KPIs, using linear programming techniques to maximize the overall profit. Process data is extracted from existing PI real-time data management system, which is normally secured by unidirectional information flow and can be easily reached by external devices.

Using the information from the process, ANACON-AI performs data acquisition, monitoring, verification, validation, statistical evaluation, correction and reporting of measurements results. Prediction of main process KPIs, such as physical properties and chemical compositions for different process streams is provided by multivariable data analysis algorithms. Deep learning and big data analytics functionality is provided by neural network learning tools and can be as option installed offline, in order to eliminate a need in continuous cloud computing.

What is an AI Engineer?

The obtained information can be used for efficiency improvement and quality improvement. The challenges of industrial AI to unlock the value lies in the transformation of raw data to intelligent predictions for rapid decision-making. In general, there are four major challenges in realizing industrial AI.

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Engineering systems now generate a lot of data and modern industry is indeed a big data environment. However, industrial data usually is structured, but may be low-quality. The quality of the data may be poor, and unlike other consumer-faced applications, data from industrial systems usually have clear physical meanings, which makes it harder to compensate the quality with volume.

Industrial data patterns can be highly transient and interpreting them requires domain expertise, which can hardly be harnessed by merely mining numeric data.


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Production process happens fast and the equipment and work piece can be expensive, the AI applications need to be applied in real-time to be able to detect anomalies immediately to avoid waste and other consequences. Cloud-based solutions can be powerful and fast, but they still would not fit certain computation efficiency requirements. Edge computing may be a better choice in such scenario. Unlike consumer-faced AI recommendations systems which have a high tolerance for false positives and negatives, even a very low rate of false positives or negatives rate may cost the total credibility of AI systems.

Why are AI Engineers important?

Industrial AI applications are usually dealing with critical issues related to safety, reliability, and operations. Besides prediction accuracy and performance fidelity, the industrial AI systems must also go beyond prediction results and give root cause analysis for anomalies. This requires that during development, data scientists need to work with domain experts and include domain know-how into the modeling process, and have the model adaptively learn and accumulate such insights as knowledge.

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