Machine Learning Enhanced LIBS to Measure and Process Biofuels and Waste Coal for Gasifier Improved Operation

Statement of the Problem - The use of biofuels and waste coal has significant environmental benefits, and it can contribute to promoting a low-carbon economy via hydrogen production. These feedstocks can be cost effective, are readily available, and in the case of biofuels are renewable with the near elimination of greenhouse gases. But there are issues with gasifying biofuels and waste coal. The first one is the widely varying organic makeup and moisture content of the feedstock which makes optimization and proper control of the gasifier very challenging. The second is the impact on the slag properties of the inert part of the fuel, affecting reactor operation and reliability. How Problem Will be Solved - To solve these issues and therefore take full advantage of these feedstocks, the project team propose to develop machine learning (ML) enhanced diagnostics to enable gasifiers to process waste coal and biofuels optimally and economically; thus, avoiding them from being landfilled and taking advantage of their value and favorable environmental properties. Laser induced breakdown spectroscopy (LIBS) along with the use of advanced ML signal processing can accurately measure important properties of the feedstock such as proximate and ultimate analyses, including moisture and ash content, elemental concentrations, heating value, and ash slagging temperatures. In a proposed system to be developed in this project, a laser beam is directed onto the raw feedstock moving on the feed conveyor belt prior to going to the gasifier. The laser vaporizes and ionizes micrograms of the feedstock. The resulting emitted radiation is collected by a spectrometer which records the wavelength and the intensity of the radiation. The wavelengths uniquely identify each element in the feedstock and the intensities are proportional to their concentrations. Next, ML processes the LIBS raw spectra and accurately determines the feedstock properties of interest. This includes higher order properties of the fuel such as heating value and ash slagging temperatures. Further, and most importantly, the measurement is made continually and in situ, thus allowing near real time input that would enable feed-forward control of the entire gasification process. What is to be Done in Phase I - In Phase I the project team will conduct laboratory tests on waste coals and biofuels, and process the data with ML to determine the instrument's capabilities and operating envelop. The team members and other stakeholders will evaluate the results and from this we will make a Go/No Go decision about submitting a Phase II proposal. Commercial Applications - The commercial focus of this project is for the instrument to be used at gasifiers which will provide real time data on their feedstock and allow them to better control their operation and optimize their performance. Other markets include the coal-fired power plant, and MSW and biofuel processing facilities.