resources

---

The purpose of Modcon.AI package is to provide process engineers with set of dynamic optimization tools, which enables connectivity, validation and prediction of main KPIs. NN-based dynamic model predicts physical properties and chemical compositions for different process streams, and proposes required process set points, that will accomplish the calculated predictions.

The purpose of Modcon-AI package is to provide process engineers with set of modern optimization tools, which enables connectivity, validation and prediction of main KPIs, to take the correct decisions. 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. Predicted products quality is continuously verified against process analyzers data and laboratory results. 

Deep Reinforcement Learning (DRL) algorithms implemented in Modcon.AI solution can be used to optimize industrial processes for different strategic goals, allowing to shift focus intelligently and confidently. Predicted products quality is continuously updated against process analyzers data to allow the simulated process’ “digital twin” to continuously be trained to reach highest possible efficiency of the process at lowest cost. 

Efficient refinery optimization includes a complex of different components, which increases process efficiency under proper operation conditions. Many refineries faces similar performance issues, which can be mathematically described to reduce the dependence on operating procedures. Modern machine and deep learning technologies predicts physical properties and chemical compositions for different process streams and incrementally improve control behavior, efficiency, productivity, and performance. 

An important step in optimizing the performance of ethylene cracker is to determine critical KPIs. Observed correlation between the different physical and chemical properties, historical data, as well as physical, chemical, and thermodynamic rules, enables mathematical models to be simplified and reduces the number of variables enabling the chemical yields of the entire process to be predicted. Process spectrometer analyzers enables to measure on-line the composition of liquid and gas feedstocks, which, when combined with furnace operation parameters, helps to predict the quality of final product and effluent. 

 Refinery in-line blending can represent an extremely cost effective and accurate method of producing bunker fuel, that meets the quality specification (typically ISO8217) and IMO 2020 environmental rules. Modcon’s turn-key solution, establishes an advanced blending system by integration of on-line process analytical results with blending simulation software. 

According to the recent research conducted by VALERO ENERGY (USA), optimized in-line blending could represent a majority of the site's total advanced process control (APC) savings, and yield in excess of $20 million/year in bottom savings. 

 Modcon.AI turn-key solution, establishes an advanced blending system by integration of on-line process analytical results with blending simulation software. 

 Further investment in the AI/ML technologies for sustainable development means for Modcon adopting business strategies and developing advanced technologies that meet the needs of the enterprise today while protecting, sustaining and enhancing the human and natural resources that will be needed in the future.