Flaring is crucial to chemical plant safety. However, excessive flaring, especially the intensive flaring during the chemical plant start-up operation, emits huge amounts of volatile organic compounds (VOCs) and highly reactive VOCs, which meanwhile results in tremendous industrial material and energy loss. Thus, the flare emission should be minimized if at all possible. This paper presents a general methodology on flare minimization for chemical plant start-up operations via plantwide dynamic simulation. The methodology starts with setup and validation of plantwide steady-state and dynamic simulation models. The validated dynamic model is then systematically transformed to the initial state of start-up and thereafter virtually run to check the plant start-up procedures. Any infeasible or risky scenarios will be fed back to plant engineers for operation improvement. The plantwide dynamic simulation provides an insight into process dynamic behaviors, which is crucial for the plant to minimize the flaring while maintaining operational feasibility and safety. The efficacy of the developed methodology has been demonstrated by a real start-up test.
The natural gas liquefaction process is an important sector of the overall liquefied natural gas (LNG) value chain. In this article, a thermodynamic-analysis-based study of the minimization of the energy consumption of a typical natural gas liquefaction process is performed. First, a rigorous simulation of the natural gas liquefaction process is conducted. According to the simulation results, the operating states of the refrigerant and natural gas streams are revealed, along with the operating conditions of the entire liquefaction process. Then, the energy consumption roadmap is determined through in-depth thermodynamic analysis, where the opportunities for energy consumption minimization are identified. Based on the thermodynamic analysis, a rigorous optimization model is developed and solved for energy consumption reduction of the same natural gas liquefaction process. Finally, the optimization results are examined again through rigorous simulation to check the feasibility of the obtained optimal solution.
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