STTR Phase I: Industrial combustion optimization using machine learning to reduce emissions and increase fuel efficiency

小型企业技术转让(STTR)第一阶段项目的更广泛影响/商业潜力将来自于开发可应用于任何工业燃烧系统的人工智能优化系统。拟议系统的模拟器组件将允许操作员在不中断操作的情况下在大型设施上运行快速、低成本的实验，优化器组件将建议设备设置，以在保持性能的同时减少燃料成本和排放。因此，该系统可能会对美国能源和制造业市场产生重大影响，使美国公司在全球范围内更具竞争力，同时减少有害污染物的产生。应用领域包括能源生产、水泥生产、钢铁制造和采矿作业。这个小型企业技术转让(STTR)第一阶段项目有望极大地促进人们对复杂工业燃烧过程的理解。目前，控制模型使用不准确的假设，造成运行效率低下，导致燃料浪费和排放增加。该项目提出了一种新的燃烧研究范式，将化学和热力学的理论严谨性与机器学习的数据自适应灵活性相结合。该公司新的分析管道的主要贡献包括一个模拟器，使操作员能够在其设施上进行电子实验，以及一个优化器，通过更精确的控制最大限度地提高燃烧效率，节省操作员的时间并降低燃料成本。这些模型利用关于设施的因果信息来训练非参数回归集合，该集合学习植物随着时间的推移的行为。拟议的项目将为开发三大支柱分析管道提供资金：无监督数据探索、监督工厂模拟和定制优化。这种方法将公认的科学原理与最先进的机器学习技术相结合，以建立快速、灵活和准确的发电厂和其他工业设施的模拟器。有了这些模型，分析师可以在计算机实验中运行，以优化热效率并训练算法，以实时推荐理想的电厂设置。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project will result from development of an artificial intelligence optimization system that can be applied to any industrial combustion system. The simulator component of the proposed system will allow operators to run fast, low-cost experiments on large facilities without disrupting operations, and the optimizer component will recommend equipment settings to reduce fuel costs and emissions while maintaining performance. As a result, the system can potentially have a major impact on US energy and manufacturing markets, making American firms more competitive worldwide while simultaneously reducing the generation of harmful pollutants. Areas of application include energy generation, cement production, steel manufacturing, and mining operations. This Small Business Technology Transfer (STTR) Phase I project promises to significantly advance the understanding of complex industrial combustion processes. Currently, control models use inaccurate assumptions that create operational inefficiencies, resulting in wasted fuel and increased emissions. This project proposes a new paradigm for combustion research that combines the theoretical rigor of chemistry and thermodynamics with the data-adaptive flexibility of machine learning. The primary contributions of the company’s novel analytics pipeline include a simulator that enables operators to run in silico experiments on their facilities and an optimizer that maximizes combustion efficiency with more accurate controls, saving operators’ time and reducing fuel costs. These models leverage causal information about the facility to train a nonparametric regression ensemble that learns the plant’s behavior over time. The proposed project will fund the development of a three pillars analytics pipeline: unsupervised data exploration, supervised plant simulation, and customized optimization. This approach combines well established scientific principles with state-of-the-art machine learning techniques to build fast, flexible, and accurate simulators of power plants and other industrial facilities. With these models, analysts can run in silico experiments to optimize thermal efficiency and train algorithms to recommend ideal plant settings in real-time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.