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

我们社会面临的最困难的挑战之一是减少温室气体排放，试图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使用化石燃料在偏远地区使用柴油发电机进行运输、供暖和发电。在这个研究计划中，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开发的用于电力生产、公路、铁路和海上运输的能效解决方案的影响。*在过去10年中，我们的研究团队为可再生能源和能效应用的新技术的开发做出了贡献。最重要的贡献是利用压缩空气储能(CAES)、相变材料储热(PCMHs)和柴油机气动混合动力(PHDE)。我们深入研究了这些技术在风力-柴油混合系统(偏远地区的电力生产)和交通运输(公路、铁路和海上)中的应用。*CAES、PCMHs和PHDE显著提高了具有压缩空气储存的风力-柴油混合系统(WDCAS)的可再生能源渗透率。在典型的WDCAS应用中，我们有更高的风能穿透率，在强风期间多余的风能被用来压缩和储存空气。在压缩过程中，热量被回收并存储在PCMH中以备将来使用。当风能不足以提供充电时，储存的压缩空气被用来给柴油过度充电，以便在每种状态下以最佳的空燃比运行。在进入发动机之前，使用PCMH对压缩空气进行加热。与无储存的风力-柴油系统相比，可再生能源在总消耗量中的百分比增加了30%-60%。*在交通运输中的应用主要包括在断裂时以CAES和PCMHs的形式进行能量回收，以及通过柴油发动机的过充来恢复这些能量，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，一个城市行驶周期(Artemis)可节省高达60%的燃料。*在完成热力学和传热分析的同时，针对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，这些解决方案在应用于工业之前存在着重大的挑战。人工智能(AI)技术将被用于根据具体应用选择涉及CAES、PCMHs和PHDE的最重要的运行参数，并建立能够优化实时运行的模型。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，以便在广泛的操作条件和约束范围内实时优化控制参数。**

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. **