Artificial intelligence for sustainable development
AI andmachine learning technologies support themodern hydrocarbon processing industry by adopting business strategies and developing artificial solutions
GREGORY SHAHNOVSKY, ARIEL KIGEL and GADI BRISKMAN Modcon Systems
T he oil and gas industry cur- rently faces the greatest envi- ronmental, health and safety challenges in its entire history. As oil and gas remains a core part of global energy for the foreseeable future, companies need to develop proac- tive and transparent sustainability strategies that maintain their licence to operate in their traditional busi- ness, whilst identifying and securing new opportunities arising from the transition to a low carbon economy. There are several factors influ - encing sustainability efforts in hydrocarbon processing to address growing issues that are perma- nently impacting the industry. Some of the biggest drivers include diversification and digitalisation, which are interlinked with each other to enable industry to reach sustainability goals. As oil and gas companies look beyond the barrel and continue to diversify, it creates more complexity within their oper- ations, which presents additional
challenges to their sustainability efforts. Launched in 2015, the World Economic Forum initiative has deter- mined digitalisation as an indisputa- ble requirement to allow the oil and gas industry to redefine its bounda - ries. The pandemic has accelerated digitalisation and companies quickly learned that they have to become more agile to respond to major dis- ruptions and demand fluctuations, when the COVID-19 crisis made demand for oil and gas disappear almost instantly. At present, the oil and gas indus- try must be flexible enough to respond immediately to feedstock changes and deviations in product demands as a result of the chang- ing global economy. Hydrocarbon processing becomes more sophisti- cated and companies have invested heavily in cleaner fuel production, molecular recycling, plastic waste reduction efforts, and more effi - cient and environmentally friendly
production methods (see Figure 1 ). Process optimisation became an undisputable requirement and the only solution to survive in competi- tive markets. Formal optimisation using linear programming was started by Leonid Kantorovich in 1939. This tradi- tional optimisation is a supervisory application that delivers optimum set points to process controllers. A major strategy in achieving this goal during the last 30-40 years was to utilise real-time optimisation (RTO) and model-predictive control (MPC), which requires continuous main- tenance to accommodate feedstock changes, process improvements, and deviations in environment conditions. The artificial intelligence (AI) and machine learning (ML) approaches started to emerge in the late 1990s as the third generation of optimisa- tion methods. The idea was to use a limited number of design trials (sam- ple points) to construct a machine
1970–1990
1940–1950 Thermal cracking Thermal reforming
Batch fractionation Continuous fractionation
Catalytic cracking Catalytic reforming Alkylation Polymeri s ation
The present-day re nery
Heavy ends conversion re nery
1920–1940
1950–1970
1990–2021
Increased throughputs and the production of multiple distillate fractions as products from a refinery
Increased the yield of light and middle distillates, i.e., gasoline, kerosene, and diesel fuel, from crude oil
Produced large quantities of LPG and witnessed the increasing demand for kerosene, now as jet fuel.
Oil crises of 1973 and 1979 created price shocks, contributed to energy efficiency and independence
Producing cleaner fuels and cleaner operation of refining processes mandated by environmental regulations
Figure 1 Evolution of refinery processes
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