Artificial Intelligence to Reduce GHG Emissions in Energy Production and Transport Applications

人工智能减少能源生产和运输应用中的温室气体排放

• 批准号: RGPIN-2019-04220
• 负责人: Ilinca, Adrian
• 金额: $ 1.43万
• 依托单位: Université du Québec à Rimouski
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759474
• 关键词: Artificial; Intelligence; Reduce; GHG; Emissions

One of the most difficult challenges facing our society is to reduce GHG emissions in an attempt to mitigate climate changes and their effect on the planet. Most of GHG emissions in Canada are from the use of fossil fuels for transportation, heating and electricity production in remote areas with diesel generators. In this research program, we specifically address these issues and apply artificial intelligence techniques to increase the impact of energy efficiency solutions developed in our research group for electricity production, road, rail and maritime transport. For the last 10 years, our research team has contributed to the development of new technologies for renewable energy and energy efficiency applications. The most important contributions were the use of Compressed Air Energy Storage (CAES), Phase Change Materials Heat Storage (PCMHS) and Pneumatic Hybridization of Diesel Engines (PHDE). We thoroughly studied the application of these technologies for the optimization of hybrid wind-diesel systems (electricity production in remote areas) and transportation (road, rail and maritime). CAES, PCMHS and PHDE significantly improve the renewable energy penetration in hybrid Wind-Diesel systems with Compressed Air Storage (WDCAS). In a typical WDCAS application we have higher wind power penetration and the surplus of wind power during strong winds is used to compress and store air. During compression, the heat is recovered and stored in a PCMHS for future use. When the wind energy is insufficient to supply the charge, the stored compressed air is used to overcharge the diesel such as to operate at an optimal air-fuel ratio at every regime. Before entering the engine, the compressed air is heated using the PCMHS. The overall renewable energy percentage in the total consumption increases between 30% and 60% compared with a wind-diesel system without storage. The application in transportation consists mainly in energy recuperation during breaking, in form of CAES and PCMHS, and restitution of this energy through overcharge of the diesel engine such as to minimize fuel consumption. The theoretical analysis based on thermodynamic models shows up to 60% fuel reduction for an urban driving cycle (ARTEMIS). While we completed the thermodynamic and heat transfer analysis, established the theoretical models for the different solutions, made a preliminary performance assessment, there are significant challenges before these solutions can be applied to industry. Artificial Intelligence (AI) techniques will be used to choose the most significant operating parameters involving CAES, PCMHS and PHDE according to the specific application and build models that can optimize real time operation. The AI models, that accurately represents the diversity and complexity of the systems and phenomena involved in these solutions, should operate sufficiently fast to optimize in real time the control parameters over a wide span of operating conditions and constraints.

我们社会面临的最艰巨挑战之一是减少温室气体排放，以缓解气候变化及其对地球的影响。加拿大大部分温室气体排放来自偏远地区使用柴油发电机进行运输、供暖和发电所使用的化石燃料。在这项研究计划中，我们专门解决这些问题，并应用人工智能技术来提高我们研究小组为电力生产，公路，铁路和海上运输开发的能源效率解决方案的影响。在过去的10年里，我们的研究团队为可再生能源和能源效率应用的新技术的开发做出了贡献。最重要的贡献是使用压缩空气储能（CAES），相变材料蓄热（PCMHS）和柴油发动机的气动混合（PHDE）。我们深入研究了这些技术在优化风力-柴油混合系统（偏远地区的电力生产）和运输（公路，铁路和海上）方面的应用。CAES、PCMHS和PHDE显著提高了可再生能源在具有压缩空气存储（WDCAS）的混合风-柴油系统中的渗透率。在典型的WDCAS应用中，我们具有更高的风力穿透率，并且强风期间的风力盈余用于压缩和储存空气。在压缩过程中，热量被回收并储存在PCMHS中以备将来使用。当风能不足以供应充气时，存储的压缩空气用于对柴油机过度充气，以便在每个状态下以最佳空燃比操作。在进入发动机之前，压缩空气使用PCMHS加热。与没有存储的风-柴油系统相比，可再生能源在总消耗中的总体百分比增加了30%至60%。在运输中的应用主要包括在制动过程中以CAES和PCMHS的形式进行能量回收，以及通过柴油发动机的过度充电来恢复这种能量，以最大限度地减少燃料消耗。基于热力学模型的理论分析表明，城市驾驶循环（ARTEMIS）的燃油减少高达60%。虽然我们完成了热力学和传热学分析，建立了不同解决方案的理论模型，并进行了初步的性能评估，但这些解决方案在应用于工业之前还有很大的挑战。人工智能（AI）技术将用于根据具体应用选择涉及CAES、PCMHS和PHDE的最重要的操作参数，并建立能够优化真实的时间操作的模型。人工智能模型准确地代表了这些解决方案中涉及的系统和现象的多样性和复杂性，应该足够快地运行，以便在宽范围的操作条件和约束条件下真实的时间内优化控制参数。