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: $ 4.01万
• 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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737490
• 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.

我们社会面临的最困难的挑战之一是减少温室气体排放，以减轻气候变化及其对地球的影响。加拿大的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、供暖和发电的化石燃料。在这个研究项目中，我们专门解决这些问题，并应用人工智能技术来提高我们的研究小组开发的能源效率解决方案对电力生产、公路、铁路和海上运输的影响。过去 10 年，我们的研究团队为可再生能源和能源效率应用新技术的开发做出了贡献。最重要的贡献是压缩空气储能（CAES）、相变材料蓄热（PCMHS）和气动混合柴油机（PHDE）的使用。我们深入研究了这些技术在风柴油混合系统（偏远地区发电）和运输（公路、铁路和海运）优化中的应用。 CAES、PCMHS 和 PHDE 显着提高了具有压缩空气存储 (WDCAS) 的风柴油混合系统中的可再生能源渗透率。在典型的WDCAS应用中，我们具有更高的风电渗透率，强风期间的剩余风电用于压缩和存储空气。在压缩过程中，热量被回收并存储在 PCMHS 中以供将来使用。当风能不足以提供充气时，存储的压缩空气用于对柴油进行过量充气，以便在每个状态下以最佳空燃比运行。在进入发动机之前，压缩空气使用 PCMHS 进行加热。与无存储的风柴油系统相比，可再生能源在总消耗中的比例增加了 30% 至 60%。交通运输中的应用主要包括制动期间的能量回收，以 CAES 和 PCMHS 的形式，以及通过柴油发动机的过度充电来恢复该能量，从而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分析，为不同的解决方案建立了理论模型，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存在重大挑战。人工智能（AI）技术将用于根据具体应用选择最重要的操作参数，包括CAES、PCMHS和PHDE，并建立可以优化实时操作的模型。准确表示这些解决方案中涉及的系统和现象的多样性和复杂性的人工智能模型应该足够快地运行，以便在广泛的操作条件和约束条件下实时优化控制参数。