Prediction of NOx Emissions from a Biomass Fired Combustion Process Based on Flame Radical Imaging and Deep Learning Techniques

This article presents a methodology for predicting NOx emissions from a biomass combustion process through flame radical imaging and deep learning (DL). The dataset was established experimentally from flame radical images captured on a biomass-gas fired test rig. Morphological component analysis is undertaken to improve the quality of the dataset, and the region-of-interest extraction is introduced to extract the flame radical part and rescale the image size. The developed DL-based prediction model contains three successive stages for implementing the feature extraction, feature fusion, and emission prediction. The fine-tuning based on the prediction is introduced to adjust the process of the feature fusion. The effects of the feature fusion and fine-tuning are discussed in detail. A comparison between various image- and machine-learning-based prediction models show that the proposed DL prediction model outperforms other models in terms of root mean square error criteria. The predicted NOx emissions are in good agreement with the measurement results

Online Dynamic Prediction of Potassium Concentration in Biomass Fuels through Flame Spectroscopic Analysis and Recurrent Neural Network Modelling

Biomass fuels are widely used as a renewable source for heat and power generation. Alkali metals in a biomass fuel have an significant impact on furnace safety as such metals lead to fouling and slagging in the furnace and corrosion of water pipes. This paper presents a technique for dynamic predicting Potassium (K) concentration in a biomass fuel based on spectroscopic analysis and different recurrent neural networks. A miniature spectrometer is employed to acquire the spectroscopic signals of K in different biomass fuels, including peanut shell, willow, corn cob, corn straw and wheat straw, and their blends. The spectroscopic features of K are extracted. The factors that influence the spectral intensity of K in the biomass fuels are investigated. A basic recurrent neural network (RNN), and its variants, i.e., long short-term memory neural network (LSTM-NN) and deep recurrent neural network (DRNN), are constructed using the spectroscopic signal of K from the spectrometer. The performances of the neural networks for the dynamic prediction of K concentration are compared and analysed theoretically and experimentally. It is found that the relative error in the K concentration prediction through the use of the DRNN model is within 6.34% whilst the LSTM-NN and RNN models give errors slightly greater than this

Feasibility of Groundwater Source Heat Pumps for Space Heating and Cooling in Mason County and the American Bottoms Area, Illinois

While air temperatures in Illinois vary greatly, shallow groundwater temperatures are nearly constant. Groundwater source geothermal heat pump systems can exploit this temperature difference for energy-efficient space heating, space cooling, and refrigeration. Such systems may contribute to energy efficiency gains and sustainable economic development. This project characterized two areas for geothermal heating and cooling potential. Mason County in central Illinois is mostly rural. The American Bottoms area of Madison and St. Clair Counties in southwestern Illinois is largely urban. Both areas are underlain by a thick sand and gravel aquifer. Although there are numerous water supply wells in both areas, groundwater is readily available for groundwater source heat pump systems. The heating and cooling requirements for a single-family house were estimated using two independent models that use weather data as input. Weather data, including monthly high and low temperatures and heating and cooling degree days, were compiled for both study areas. The groundwater pumping rates for these heating and cooling requirements were then calculated. The performance of a heat pump is expressed as the coefficient of performance (COP), the ratio of heating or cooling rate to the electrical energy input. For groundwater heat pumps, the heating COP value is 3.0 to 4.0. For cooling, COP ranges from 3.5 to 6.7. Calculations were performed using these ranges of COP. The groundwater in both study areas has fairly high hardness and iron concentrations and is close to saturation with calcium and iron carbonates. Using the groundwater for cooling will probably induce the precipitation of moderate amounts of one or both of these minerals. Periodic cleaning of heat exchanger surfaces, other system piping, and possibly well screens will be needed to remove these deposits.This project was supported by the Illinois Sustainable Technology Center, a division of the Prairie Research Institute at the University of Illinois at Urbana-Champaign (Grant No. HWR12225).Ope

Biomass Fuel Identification Based on Flame Spectroscopy and Feature Engineering

Flame spectra contain useful information about combustion and hence the spectral features of flame radicals may be used to identify different biomass fuels. A technique for biomass fuel identification is proposed based on the spectral features of flame radicals, feature engineering and improved support vector machine. The spectral intensity signals of biomass flames and flame radicals (OH*(310.85nm), CN*(390.00nm), CH*(430.57nm) and C2*(515.23nm, 545.59nm)) were acquired using a spectrometer. Feature engineering was built, which can accurately reflect the characteristics of sample category, through feature extraction, feature selection based on Filter and feature learning based on dictionary learning. The support vector machine is used to build the identification model, where radial basis kernel parameter ? and error penalty factor C are optimized using an improved grid search algorithm. Experimental results from a laboratory-scale combustion rig show the effectiveness of the proposed method for the identification of biomass fuel

Measurements in degassing processes of CO2_{{2}} solution with particular reference to CO2_{{2}}-driven limnic eruptions

CO2-driven limnic eruptions are lethal phenomena that occur in lakes with aqueous CO2 solutions that become supersaturated. The exsolution of massive CO2 dissolved in the water can happen in a very short time, possibly leading to a natural disaster as happened in the Lake Nyos (Cameroon, Africa) in 1986. More than 1700 people died. In recent years, with the utilization of the technology of CO2 sequestration in brines in geological reservoirs, there are possibilities of the CO2-brine leakage. The brine may stay in the near surface water leading to the potential of an eruption. In this experimental study, measurements have been carried out to investigate the degassing processes of CO2 solutions under different depressurizing conditions. Based on the experimental data and using the ImagePro Plus® to process the recorded images, two correlations have been obtained: (1) the relationship between the supersaturation (${\Delta }P$) required for degassing and the initial pressure; (2) the relationship between the time delay (${\Delta }t$) corresponding to bubble formation and the initial pressure. Variations of key quantities (void fraction, number of bubbles, and average diameter of bubbles) over time have been analyzed. In addition, the void fractions measured in two different depressurizing ways have been compared. The experimental data and correlations obtained in this study are useful in establishing transient fluid dynamic models for simulating CO2-driven eruptions

Measurements in degassing processes of CO2_{{2}} solution with particular reference to CO2_{{2}}-driven limnic eruptions

CO2-driven limnic eruptions are lethal phenomena that occur in lakes with aqueous CO2 solutions that become supersaturated. The exsolution of massive CO2 dissolved in the water can happen in a very short time, possibly leading to a natural disaster as happened in the Lake Nyos (Cameroon, Africa) in 1986. More than 1700 people died. In recent years, with the utilization of the technology of CO2 sequestration in brines in geological reservoirs, there are possibilities of the CO2-brine leakage. The brine may stay in the near surface water leading to the potential of an eruption. In this experimental study, measurements have been carried out to investigate the degassing processes of CO2 solutions under different depressurizing conditions. Based on the experimental data and using the ImagePro Plus® to process the recorded images, two correlations have been obtained: (1) the relationship between the supersaturation (${\Delta }P$) required for degassing and the initial pressure; (2) the relationship between the time delay (${\Delta }t$) corresponding to bubble formation and the initial pressure. Variations of key quantities (void fraction, number of bubbles, and average diameter of bubbles) over time have been analyzed. In addition, the void fractions measured in two different depressurizing ways have been compared. The experimental data and correlations obtained in this study are useful in establishing transient fluid dynamic models for simulating CO2-driven eruptions

Ionic cluster effect in suppression on superconductivity in Ni- and Co-doped YBCO systems

We adopted the x-ray diffraction, oxygen contents, positron annihilation technology as well as simulation methods to investigate systemically YBa₂Cu₃–x(Ni,Co)xO₇–δ (x = 0–0.5). The simulated results show that ions distribute in dispersive form in little doped concentration. As doped concentration increases, ions combine into clusters in the crystal lattice. The calculated results and oxygen contents, together with the impure phases and the local electron density ne, show the ionic cluster effect, which not only causes the local electron density to reach the saturation, but also suppress the superconductivity significantly

The Nell-1 Growth Factor Stimulates Bone Formation by Purified Human Perivascular Cells

The search for novel sources of stem cells other than bone marrow mesenchymal stem cells (MSCs) for bone regeneration and repair has been a critical endeavor. We previously established an effective protocol to homogeneously purify human pericytes from multiple fetal and adult tissues, including adipose, bone marrow, skeletal muscle, and pancreas, and identified pericytes as a primitive origin of human MSCs. In the present study, we further characterized the osteogenic potential of purified human pericytes combined with a novel osteoinductive growth factor, Nell-1. Purified pericytes grown on either standard culture ware or human cancellous bone chip (hCBC) scaffolds exhibited robust osteogenic differentiation in vitro. Using a nude mouse muscle pouch model, pericytes formed significant new bone in vivo as compared to scaffold alone (hCBC). Moreover, Nell-1 significantly increased pericyte osteogenic differentiation, both in vitro and in vivo. Interestingly, Nell-1 significantly induced pericyte proliferation and was observed to have pro-angiogenic effects, both in vitro and in vivo. These studies suggest that pericytes are a potential new cell source for future efforts in skeletal regenerative medicine, and that Nell-1 is a candidate growth factor able to induce pericyte osteogenic differentiation