Decision Support
❯
Diagnose
❯Operations - Intelligent Monitoring of Infrastructures
Large Companies
Knowledge representation - Bayesian Network
For:
Process Engineers.
Scope:
Applying Bayesian Networks to root cause analysis of industrial processes.
Goal:
Improve Operation Efficiency
Large Companies
Operations - Intelligent Monitoring of Infrastructures
For:
Plant Operators
Scope:
Optimization of thermal efficiency of a power plant using Machine Learning.
Goal:
Improve Operation Efficiency, Improved Employee Efficiency, Reduce costs
General Public
Healthcare - Improve Investigatory Work
For:
Radiologists
Scope:
Using machine learning to predict lung cancer from CT scans.
Goal:
Anticipate Risks
Prediction of Multidrug-Resistant TB from CT Pulmonary Images Based on Deep Learning Techniques
For:
Clinicians and medical professionals predicting multidrug-resistant (MDR) patients from drug-sensitive (DS) ones based on CT lung images.
Goal:
Improved Employee Efficiency
Large Companies
Operations - Intelligent Monitoring of Infrastructures
For:
Plant OperatorsGoal:
Improve Operation Efficiency, Improved Employee Efficiency, Reduce costsProblem addressed
Twenty-four-seven monitoring of energy infrastructure, such as power plants, oil rigs or wind farms, is a challenging endeavor requiring expert operators and process engineers. Sensors are applied to equipment to send operating data back to monitoring systems.
Often 100s of variables have to be tuned and alerts are typically set point based, making use of thresholds applied using the expertise of the operator. To complicate matters further, alerts need to be attended to in a timely manner in order to ensure issues do not escalate further.
Scope of use case
Optimization of thermal efficiency of a power plant using Machine Learning.
Description
The large amount of historical data collected by sensors makes this scenario a perfect case for optimization using machine and deep learning. Automatic setting and adjusting of set points allows not only for optimisation of cost, but also frees up the time of subject matter experts to concentrate on responding to critical situations.
The Vistra owned Martin Lake power plant in Tatum, Texas, partnered with Mckinsey to deliver such a Machine Learning optimisation approach. In order to optimize the thermal efficiency of the plant, a neural network trained on 2 years worth of plant data was built. Once the model was deployed it was used to generate efficiency recommendations to operators every 30 minutes.
Vistra then proceeded to roll out the approach at a further 26 plants, achieving $23m in savings and 10% of its remaining carbon abatement target for 2030.
Sensor Network - IOT
AI: Perceive
Machine Learning
Deep Learning
AI: Understand
Diagnose
Optimize
Recommend
AI: Act