The evolution of process optimisation
Launched in 2015, the World Economic Forum initiative has determined digitalisation as an indisputable requirement, resulting in the oil and gas industry taking a fresh look at its boundaries. The pandemic has accelerated digitalisation, and companies quickly learned that they had to become more agile to respond to significant disruptions and demand fluctuations when the COVID-19 crisis diminished demand for oil and gas almost instantly.
At present, the oil and gas sector must be flexible enough to respond immediately to the changes in feedstock product demand as a result of the changing global economy. The hydrocarbons industry has become more sophisticated, with heavy investment in cleaner fuel production, molecular recycling, plastic waste reduction efforts, and more efficient and environmentally-friendly production methods. Economic process optimisation is now a fundamental requirement and the only solution to survive in competitive markets.
In order to fully exploit the economic optimisation potential of a technological process, the approach of working in the paradigm of optimising control should be taken, as opposed to addressing optimisation and control problems as two separate tasks. This article demonstrates how the trend for convergence of process optimisation and multivariate control has evolved, and discusses the computational roadblock that is standing in its way. It also explores how artificial intelligence (AI) can overcome these constraints without compromising the process modelling fidelity.