FeatureArtificial intelligence learning provides data-driven approaches, that is data writes the programme and its conclusions instead of human programming. Artificial intelligence-machine learning emulates human capabilities to recognise patterns and from them infer possibilities or probabilities of future events. But artificial intelligence-machine learning detects correlation and not necessarily causation.
The most significant ROI is the total profitability of the operation
Massive transformations result from guide rails around artificial intelligence that apply domain and first principles causation, superior knowledge feature selection, improving training data sets which then deliver better deductive results with significantly fewer false positives. Recognising errant mechanical and process conditions is only the start. Deeper artificial intelligence-machine learning investigations are now deducing the root causes of why those conditions recur at all.
Some early best practices for artificial intelligence begin with stopping machines from breaking and are well recognized by startups, consulting groups, and large companies in process and maintenance in asset performance management APM solutions. Artificial intelligence leaders have progressed into managing operations, supply-chain optimisation. All such activities aim to improve lifecycle overall equipment effectiveness.
At the start of machine learning, the most apparent vertical markets are asset intensive, process industries with high throughputs
The most significant ROI is the total profitability of the operation, Return on Capital Expended. Other ROI measures include reductions in safety and environmental events, improved maintenance performance such as fewer unexpected failures or break-in events, and reduced labor-intensive periodic inspections.
At the start of machine learning in manufacturing industries, the most apparent vertical markets are asset intensive, process industries with high throughputs. They can often be dangerous when failures occur. Such verticals include oil and gas, bulk chemicals, utilities, metals, and mining.
With democratisation of artificial intelligence, all manufacturing verticals can take advantage of artificial intelligence
As the technology and solutions developed with the leaders providing extreme ease-of-use, the democratisation of artificial intelligence, and scalability, it becomes evident that all manufacturing verticals can take advantage of artificial intelligence, machine learning to improve operations and supply-chain conditions. Consequently, today we recognise opportunities in transportation, specialty chemicals, pharmaceuticals, water and wastewater, cement, food, beverage, and others.
Some of the products include:
Mtell machine learning-based precise pattern recognition detects errant behavior and exact failure signatures allowing corrective actions months before impending failure. ProMV’s artificial intelligence adjusts chemical processes correcting impending quality and yield issues. Artificial intelligence in Event Analytics quickly recognises and enables corrections of NOT OK process behavior, avoiding disruptive or out of control conditions.
ROI measures include reduction in safety events, improved maintenance, fewer unexpected failures, reduced periodic inspections
Artificial intelligence recognises manufacturing data patterns unseen by the human eye to guide superior operating campaigns HYSYS: artificial intelligence-enabled hybrid simulation models first principles enhanced by machine learning provide digital twins for the virtual operation of equipment and processes in the safe digital domain to explore, test alternative operating modes efficiently.
Domain and first principles causation, superior knowledge features, improving training data sets, deliver better results with fewer false positives.
