SOLUTIONS

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MODCON AI CDU Optimization Suite leverages AI models' capabilities to replicate complicated relationships between technological process parameters to find the most appropriate setpoints for the present state of the crude oil distillation process. The dynamics of the distillation process depend significantly on the composition of crude oil. This dependency is especially significant during the periods of crude switch – change in the properties of the feedstock. The periods of feedstock properties fluctuations are when MODCON 4100 Crude Oil NIR Analyzer provides a synergistic benefit. The analyser continuously informs the AI-based process control model of MODCON AI CDU Optimisation Suite of the current properties of crude oil.

 MODCON AI Energy Conservation applies the same process modelling and AI-driven control techniques behind MODCON AI CDU Optimization Suite to a wide range of applications in the processing industries that could not have been adequately addressed using the legacy RTO/APC technology. Numerous technological processes in petrochemical, pulp and paper, water treatment and other verticals can be challenging to represent using first principles models that are the backbone of legacy process optimisation solutions. By switching the process model to data-driven or hybrid models, a lot of value can be unlocked by reducing energy consumption in multiple energy-intensive technological processes.

Since the COVID-19 epidemic, Modcon has been developing contactless vital signs evaluation technology based on image photoplethysmography principles. The processing of the physiological signals involves advanced algorithms and Machine Learning models. A prototype of the product is undergoing an evaluation with an international customer. Another application, developed in cooperation with a children's hospital and funded by a public grant, is suicide risk detection in adolescents. The vital signs we support include HR, HRV, BP, and SpO2.

While modelling complicated relationships is necessary for the optimisation of the process, once learned, these relationships allow for identifying situations when the dynamics of the process deviate from their usual ones. The approach enables us to leverage the knowledge of the process initially obtained to find its optimal state to detect possible faults and deterioration in the equipment. Early detection of dangerous trends allows taking action before the process failure or equipment breakdown happens.

Wastewater treatment is estimated to consume 2 - 3% of a developed nation’s electrical power. The largest proportion of energy is used in biological treatment, generally in the range of 50 - 60% of plant usage. That represents an opportunity for process optimization and requires practical solutions to water pollution and water scarcity. 

The first prerequisite for an effective implementation of a production optimization solution is access to high fidelity data from a variety of different sources such as well data, production data, reservoir modeling data, field infrastructure data, and financial and economic planning data for the production business.