Many associate the fourth industrial revolution with further expenditure – instalment of new sensors to collect data, investment in data storage or perhaps in 3D printing equipment – rather than cost savings. Fortunately, the reality doesn’t always need not be this expensive. In fact, manufacturers are the ones most capable of achieving tangible value in a matter of months, with no capital investment, just simply by using machine learning for optimisation.

Relying on mathematical models

Using mathematical models to improve existing processes is not an innovative approach for manufacturers. In fact, with decades invested in this way of working, manufacturing is arguably one of the industries that does this best. Accustomed to the use of models in this way, manufacturers can simply ‘plug-in’ new technologies like AI to old processes, but deliver a transformative leap in quality.

Existing automation tools rely on knowledge-based models, built on physical equations or statistical analysis. Whilst they are precise enough, the analysis is still unable to fully reflect all of the complexities and uncertainties of real-world processes. Machine learning, on the other hand, can do just that. By analysing historical data of specific equipment or a given process, a model can learn to account for all fluctuations and deviations, and make better decisions to optimise the required KPIs. For example, the adjustment of unit parameters in real-time to decrease energy consumption while maintaining the output.

In practical terms, the ability to account for all hidden parameters and unknown patterns means an extra 5 to 10 percent in efficiency for a selected process. All without the need to change equipment, technology or teach the operators to deal with new controls, leaving them responsible for the outcome. For capital and energy intensive industries that have spent the last century on continuous improvements across the factory floor, this is indeed a breakthrough.

And here lies the reason why manufacturing is bound to lead in the AI revolution. Industrial companies inherently understand the value of optimisation, and already have in place many stable processes that have run for years without change. This is the best fuel for machine learning to showcase its power: thousands of examples to learn from, so that AI can go beyond existing models in its ability to predict future scenarios and prescribe the best action.

Collecting the low-hanging fruits

Despite predictive maintenance being hailed as the darling of machine learning use cases, the list of potential applications should not be limited by it. One example is virtual sensors which measure the unmeasurable and deduct the required properties based on available data. For example, estimating the temperature of a particular plate in a distillation column, which couldn’t otherwise be measured due to the absence of physical sensors.

Another application is the optimisation of processes where the decisions rely on the aforementioned physical models, and often require an experienced operator to make the final adjustment. For example, catalytic cracking in oil refining or ore beneficiation in mining, where there is a need to optimise the amount of catalysts or reagents used to maintain an output.

Finally, quality prediction. If potential defects can be foreseen, an optimal production route can be chosen to treat those, or a different grade of product made, to maximise the overall output and decrease costs.


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