Digital Twin of an Open Cooling Tower: Experimental Studies and Numerical Validation

IF 5.1 Q2 ENGINEERING, CHEMICAL
Lilly Zacherl,  and , Thomas Baumann*, 
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引用次数: 0

Abstract

Increasing dynamics in surface water bodies and all-time low groundwater levels, both a consequence of global warming, put high stress on the water supply chain and require a re-evaluation of all water uses. Industry uses a significant amount of water for cooling, often in open cooling towers. We developed a digital twin for an evaporative open cooling tower, focusing on the hydrochemistry to optimize water consumption and use of inhibitor chemicals to prevent scaling. The model is based on the USGS hydrochemical standard model PhreeqC, which is controlled by Python scripts. The digital twin implements evaporation in the cooling tower, recharge of water with added inhibitors, and desalination to avoid corrosion. In contrast to previous operation strategies, which rely on a thickening ratio that can be measured using the electrical conductivity, the model allows prediction of the behavior of the cooling tower based on the saturation index for the mineral precipitates. Additionally, the digital twin offers the option of controlling the cooling tower. We present a workflow to adapt the digital twin to the actual cooling tower and to parametrize the chemicals used for the prevention of mineral scaling. The optimization objectives were to reduce the consumption of inhibitors while maintaining stable hydrochemical conditions and a benign corrosion behavior. Additionally, the digital twin should reveal possibilities for demand-driven load balancing. After site-specific adaptation of flow and evaporation rates, volumes, temperatures, and equilibrium constants for the inhibitors, the model was able to forecast the hydrochemical conditions in the cooling tower. The parameter and sensitivity analyses revealed that the total volume of water in the system and the thickening ratio have a large effect on water consumption. While slightly increased concentrations of the inhibitor would allow for significantly higher thickening ratios and slightly lower water consumption, the corrosion stability of the materials in the cooling system puts limits on this approach. Evaporation remains the main factor in water consumption. For the reference site, the digital twin revealed that the implemented operation scheme was already close to optimal conditions, considering water consumption and the use of inhibitors.

开放式冷却塔的数字孪生:实验研究与数值验证
由于全球变暖,地表水体的动态变化不断增加,而地下水水位空前低,这给供水链带来了巨大压力,需要对所有用水进行重新评估。工业通常在开放式冷却塔中使用大量的水进行冷却。我们为蒸发式开放式冷却塔开发了一个数字双胞胎,专注于水化学,以优化水消耗和使用抑制剂化学物质,以防止结垢。该模型基于USGS水化学标准模型PhreeqC,由Python脚本控制。数字孪生在冷却塔中实现蒸发,添加抑制剂的水补给,以及海水淡化以避免腐蚀。以前的操作策略依赖于可以使用电导率测量的增稠比,与之相反,该模型允许基于矿物沉淀的饱和指数预测冷却塔的行为。此外,数字双胞胎还提供了控制冷却塔的选项。我们提出了一个工作流程,使数字孪生适应实际冷却塔,并参数化用于防止矿物结垢的化学品。优化的目标是在保持稳定的水化学条件和良好的腐蚀行为的同时减少抑制剂的消耗。此外,数字孪生应该揭示需求驱动的负载平衡的可能性。在对抑制剂的流量和蒸发速率、体积、温度和平衡常数进行了特定的调整后,该模型能够预测冷却塔内的水化学条件。参数分析和敏感性分析表明,系统总水量和增稠比对耗水量影响较大。虽然稍微增加缓蚀剂的浓度可以显著提高增稠率,并稍微降低水的消耗,但冷却系统中材料的腐蚀稳定性限制了这种方法。蒸发仍然是耗水量的主要因素。对于参考场地,数字孪生显示,考虑到用水量和抑制剂的使用,所实施的操作方案已经接近最佳条件。
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来源期刊
ACS Engineering Au
ACS Engineering Au 化学工程技术-
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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