改善波动的废气发电

P. Venter, S. Terblanche, M. van Eldik
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引用次数: 5

摘要

工程工厂通常有各种相互关联的生产链,其中流程流依赖于上游事件,并通过默认或手动设置进行操作。可燃废气大多被用作能源。原料进料可能随时间波动,导致废气产量波动,并可能导致能源利用效率低下。在锅炉房中,从废气中产生蒸汽是常见的做法,多余的蒸汽被分配用于发电。随着时间的推移,这些波动的废气和因此产生的蒸汽可能导致涡轮机跳闸。另一个问题是,未使用的废气被燃烧到大气中,在那里,能量潜力被抵消了。本文通过模拟典型电厂人工操作程序和优化控制算法可能产生的潜在和额外发电效果,研究了这些废气的更有效利用。这项研究是建立在这两种不同模拟方法的结果之上的。模拟结果表明,与电厂目前的运行理念相比,在控制算法下运行将产生3.67%的发电量增加。在额外增加65吨锅炉房和额外增加15MW涡轮机的假设下,模拟结果显示,在工厂运行程序下,与初始工厂设置相比,潜在发电量增加了25.19%。在调查的时间段内,控制算法产生的发电量额外增加了34.39%。在除一个模拟场景外的所有模拟场景中,控制算法都减少了涡轮机的跳闸。所有的模拟都是基于真实世界的数据。
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Improving power generation from fluctuating off-gas productions
Engineering plants typically have a variety of interlinked production chains, where process flows are dependent on upstream events and operated by default or manual settings. Burnable off-gasses are mostly utilized as energy sources. Raw material feeds may fluctuate over time, resulting in fluctuating off-gas production and potentially in inefficient energy resource usage. It is common practice to generate steam from off-gas, in boiler houses, where excess steam is allocated for power generation. Over time these fluctuating off-gas and therefore steam production may lead to turbine trips. Another problem is that unused off-gasses are burned to atmosphere, where the energy potential is nullified. This paper investigates the more efficient utilization of these off-gasses, through simulating the potential and additional power generation effect that could have occurred for a typical plant's manual operating procedure and an optimization control algorithm. The investigation is founded on the results from these two different simulation approaches. Simulated results showed that operations under the control algorithm would have yielded a 3.67% increase in power generation, when compared to the plant's current operating philosophy. Under the assumption of an additional 65 tonlh boiler house and an extra 15MW turbine, simulation results showed a potential power generation increase of 25.19% under the plant operational procedure, when compared to initial plant set up. The control algorithm yielded an additional 34.39% increase in power generation over the investigated time period. In all but one simulation scenario did the control algorithm result in less turbine trips. All simulations are based on real world data.
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