A 98% reduction in fuel gas consumption in the sulfur recovery process is one of the distinct results of using artificial intelligence in refineries. In the energy world, natural gas is recognized as an important fossil fuel source that faces challenges such as demand fluctuations and uncertainty in consumption levels. In this context, errors in forecasting and allocating gas can lead to increased costs and energy waste.
Optimizing Gas Consumption with Artificial Intelligence Algorithms
Recent research has shown that the use of artificial intelligence and machine learning algorithms can help optimize gas consumption in refineries. One significant study focused on sulfur recovery units (SRU) in refineries and demonstrated that by optimizing temperature and processes, gas consumption can be significantly reduced. In this study, researchers used Chemkin Pro and Aspen Hysys software in a MATLAB environment to create a multi-objective optimization process.
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Using a genetic algorithm, this model was able to reduce the furnace temperature to 1028 degrees Celsius and bring the fuel gas consumption in the furnace down from 1773 normal cubic meters per hour to 29 normal cubic meters per hour. This change represents a nearly 98% reduction in fuel gas consumption and has led to a decrease in the operational costs of the refinery.
Challenges and Implications of Demand Forecasting
Accurate forecasting of natural gas demand is one of the key aspects of energy planning. In various studies, older models have shown high errors in some cases due to limitations in analyzing nonlinear behaviors. For example, in a study in Turkey, the grey wolf model was used as an advanced method for forecasting and provided results with adequate accuracy.
Additionally, other algorithms such as random forests and hybrid models have been used in China to examine the impact of various factors on natural gas consumption. These methods have shown that they can provide accurate forecasts and, consequently, help optimize energy consumption.
Ultimately, the use of artificial intelligence algorithms not only helps reduce gas consumption in refineries but can also increase the capacity to accommodate renewable energies such as wind energy. Thus, optimizing gas consumption can be proposed as an effective solution for reducing costs and improving energy efficiency in the refinery industry.
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