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: $ 2.58万
• 依托单位: École de technologie supérieure
• 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=759475
• 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量来自用柴油发电机在偏远地区运输，供暖和电力生产的化石燃料。在该研究计划中，我们专门解决了这些问题，并采用人工智能技术来增加电力生产，道路，铁路和海上运输研究小组中开发的能源效率解决方案的影响。在过去的10年中，我们的研究团队为开发了可再生能源和能源效率应用的新技术做出了贡献。最重要的贡献是使用压缩空气储能（CAE），相变材料储能（PCMHS）和柴油发动机的气动杂交（PHDE）。我们全面研究了这些技术在优化混合风柴油系统（偏远地区的电力生产）和运输（道路，铁路和海上）中的应用。 CAES，PCMHS和PHDE可以显着改善具有压缩空气存储（WDCAS）的混合风柴油系统中可再生能源渗透。在典型的WDCAS应用中，我们具有更高的风能渗透率，强风中风能的盈余用于压缩和储存空气。在压缩过程中，将恢复热量并将其存储在PCMHS中，以备将来使用。当风能不足以供电时，将使用储存的压缩空气向柴油机过度充电，例如在每个制度下以最佳的空气燃料比率运行。进入发动机之前，使用PCMHS加热压缩空气。与没有存储的风柴油系统相比，总消费量的总体可再生能源百分比在30％至60％之间增加。运输中的应用主要由破裂过程中的能量恢复组成，以CAES和PCMH的形式进行，以及通过柴油发动机的过度充电来恢复这种能量，例如最大程度地减少燃油消耗。基于热力学模型的理论分析显示，城市驾驶周期（Artemis）的燃料减少高达60％。当我们完成热力学和传热分析时，建立了不同解决方案的理论模型，进行了初步性能评估，在将这些解决方案应用于行业之前，面临重大挑战。人工智能（AI）技术将根据特定的应用程序和构建模型来选择涉及CAE，PCMHS和PHDE的最重要的操作参数，以优化实时操作。准确地代表这些解决方案中涉及的系统和现象的多样性和复杂性的AI模型应足够快地运行以实时优化控制参数在广泛的操作条件和约束中。