Estimating natural gas demand at a petrochemical complex
A robust model has been developed to predict a plant’s natural gas demand, accounting for fluctuating capacities and anticipated fuel gas and steam consumption/production.
Uğurcan Tozar, Mert Akçin, Murathan Bağdat, Dila Gökçe Kuzu, Nesip Dalmaz and Kemal Burçak Kaplan
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Petkim is a petrochemical complex in Izmir, Türkiye, operated by SOCAR Türkiye. More than 10 plants are operating in the complex. Energy sources at these plants consist of natural gas, steam (generated by using natural gas), and electricity. Natural gas is one of the most critical energy sources. Predicting natural gas consumption in a day ahead period has a significant role in preparing natural gas consumption plans.
Natural gas is consumed by three main facilities, including gas turbines, aromatics, and steam generation plants. In the gas turbine, natural gas is combusted to generate electricity and heat.
Waste heat is used to preheat the boiler feed water transferred to the steam generation plant. The aromatics plant is also one of the natural gas consumers. Natural gas is mixed with the fuel gas in the plant, and the natural-fuel gas mixture is combusted in the furnaces.
The steam generation plant accounts for a significant share of fuel consumption. Steam supplied by this plant to the complex is generated by combustion of the fuel gas, consisting of a mixture of plant-produced fuel gas and natural gas. A detailed process description is mentioned in the energy balance section.
The main point with modelling the natural gas demand of the petrochemical complex is based on many operational variables, such as plant capacities and ambient temperature. In this study, the effect of plant capacities on steam consumption is analysed in detail. Fluctuation in natural gas consumption is caused by the change in the ambient temperature.
Key aspects of natural gas demand modelling
Navigating the complexities of modelling the natural gas demand for such an expansive petrochemical complex involves accounting for numerous operational variables. Key among them are the varying capacities of individual plants and the ever-fluctuating ambient conditions, particularly temperature.
This investigative study focuses on the impact of plant capacities on steam consumption. Furthermore, it delves into how meteorological shifts, specifically in temperature, are served as catalysts, inducing ripples in natural gas consumption patterns.
Every single consumer has been modelled by considering specific operational constraints (see Figure 1).
Natural gas is used by a gas turbine to generate electricity and waste heat. The primary operational constraint affecting this equipment’s efficiency is ambient temperature. The consumed natural gas is modelled by considering generated electricity and ambient temperature as dependent variables. The electricity load of the turbine is independent of the complex’s energy demand.
It also operates approximately 20-60 MWh load by considering the ambient temperature. The electricity generation capacity of the gas turbine is limited by higher temperatures, especially those with a high humidity content. In gas turbine systems, a significant portion of the provided energy to the gas turbine is lost as waste heat. In the system, the boiler feed water used in steam generation is preheated by the utilised waste heat.
When modelling the energy balance in the gas turbine system, it is known that 40-70% of the supplied energy is lost as waste heat. This approach is employed when optimising steam generation systems in the shutdown cases of the gas turbine system. In a shutdown case, the waste heat energy used to preheat the boiler feed water is compensated by consuming more energy in the steam generation plant.Steam generation plant (boiler)
Steam is used in a petrochemical complex for various heat sources, such as heating and power generation. The primary steam generation source is boilers established in the Petkim Steam Generation Plant. Extra high-pressure steam is produced in this plant by considering the complex’s steam demand. Steam is generated by firing fuel gas, a mixed gas consisting of side product fuel gas and natural gas.
The steam demand of the complex is modelled as a function of plant capacity and ambient temperature. As a result of detailed modelling explained in the modelling and simulation phase, overall steam demand is computed. In addition to steam demand, plants’ fuel gas production capacity is analysed. Hourly fuel gas production and steam demand are estimated.
The combustion of 1.0 ton/h natural gas is equivalent to producing an assumed amount of high-pressure steam, considering the efficiency of boilers. The overall natural gas demand of boilers can be calculated, and internally produced fuel gas routed to the boilers is subtracted from the total energy demand used to produce steam. While doing energy balance calculations, the enthalpy of steam and internally produced fuel gas are considered constant values.
The operation of the process furnaces within the aromatics plant is facilitated by employing a combination of internally generated fuel gas and natural gas. The formulation of the produced fuel gas depends on factors such as plant capacity and ambient temperature. In the initial phase, an analytical model is constructed to characterise the fuel gas consumption specific to the aromatics plant. Subsequently, an examination is conducted to ascertain the potential correlation between fuel gas and natural gas demand. As anticipated, a discernible positive correlation emerges between natural gas consumption and fuel gas utilisation. Consequently, the amalgamation of fuel gas and natural gas is orchestrated and subsequently used as the composite fuel source for the process furnaces.
Selection criteria for modelling production and consumption quantities
Production and consumption quantities were meticulously hand-picked in the quest for a comprehensive modelling approach. This selection specifically targeted quantities that either exhibited a pronounced consumption within the fuel gas or steam ring or showed noteworthy variability, including:
• Determinants of choice: The potential quantity’s contribution within the ring was scrutinised. Only those that occupied a significant position were earmarked for modelling
• Interrelation with other units: A thorough analysis was undertaken to examine the interplay of the considered quantity with other operational units
• Consumption variability: If a particular quantity held importance within the ring and demonstrated substantial variability in its consumption, it was flagged for further modelling endeavours.
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