In this paper a low-cost, practical pixel-based flame spectrum and temperature estimation system based on color images is proposed. A spectral resolution of ∼ 0.4 nm achieved with an optical formed by camera, linear model, flame's training data, reconstruction procedure. As proof concept, the estimated spectra are compared to local measurements performed commercial spectrometer. order estimate absolute flame-temperature maps, two radiometric at different wavelengths reconstructed two-color...

In this paper, we report on spectral features emitted by a reaction shaft occurring in flash smelting of copper concentrates containing sulfide minerals such as chalcopyrite (CuFeS2), bornite (Cu5FeS4) and pyrite (FeS2). Different combustion conditions are addressed, sulfur-copper ratio oxygen excess. Temperature emissivity estimated for each case using the two wavelength method radiometric models. The most relevant results have shown an increasing intensity behavior higher ratios contents,...

A nonintrusive low-cost sensor based on silicon photodiode detectors has been designed to analyze the formation and behavior of excited CH(*) C(2)(*) radicals in combustion process by sensing spectral emission hydrocarbon flames. The was validated performing two sets experiments for both nonconfined confined For a oil flame, responses axial intensity were highly correlated with measurements obtained radiometer. gas flames ratio between signal corresponding successfully CO pollutant emissions...

The project focused on the investigation of new sensing techniques in copper production industry, specifically flash smelting concentrate process. In this paper, we report a direct relationship between visible and near-infrared emission spectra combustion concentrates by changing some operating conditions such as sulfur-copper oxygen ratios provided to reaction zone. Spectral processing are applied measured spectra. first one aims separate both continuous radiations mainly associated...

In this paper, a novel automated algorithm to estimate and remove the continuous baseline from measured flame spectra is proposed. The estimates background based on previous information obtained learning database of spectra. Then, discontinuous emission calculated by subtracting estimated spectrum. key issue subtending that emissions are predominant in sooty regions, absence radiation. proposed was tested using natural gas bio-oil flames at different combustion conditions, goodness-of-fit...

This work presents a non-parametric method based on principal component analysis (PCA) and parametric one artificial neural networks (ANN) to remove continuous baseline features from spectra. The estimates the set of sampled basis vectors obtained PCA applied over previously composed spectra learning matrix. method, however, uses an ANN filter out baseline. Previous studies have demonstrated that this is most effective for removal. evaluation both methods was carried by using synthetic...

Optimization of combustion processes holds the promise maximizing energy efficiency, at same time lowering fuel consumption and residual gases emissions. In this context, current common operation setting in could be improved by introduction passive optical sensors, which can located close flame, thus eliminating inherent transport delay setups that only infer quality measuring However, there is a tradeoff for flame detection between spatial-spectral resolutions, depending on sensor scheme....

In this work, we present the fundamentals of estimation energy efficiency in an industrial coal boiler based novel optical combustion diagnostics variables and several machine learning regression methods. The total radiation Radt flame temperature Tf were considered. inclusion those allows to increase overall performance efficiency. comparison tested methods for regression, suggest that Extreme Learning Machines combination with Partial Least Squares lead best a Pearson correlation...

In this work, the theoretical fundamentals and experimental results of combustion control for a ladle furnace preheating process are presented, featured by high fuel consumption their subsequent operational temperatures. The maximize ladle's inner refractory temperature, via combustion. highlights closed loop solution flame spectrum analysis, calculation set optical variables optimization using information. These temperature radiation, used as inputs an inferential sensor key variable:...

In this paper, we present a method for hyperspectral retrieval using multispectral satellite images. The consists of the use training spectral data with compressive capability. By principal component analysis (PCA), proper number basis vectors are extracted. These properly combined and weighted by sensors’ responses from visible MODIS channels, achieving as result Once channels used retrieval, spectra projected over recovered data, ground-based process can be reliably detected. To probe...

We present a study for optimization of combustion process based on optical measurements. First, we adapt data-driven Hammerstein model and key variables in laboratory scale boiler. then discuss the concepts diagnosis, flame spectrum analysis inclusion control system. compare boiler manual operation, conventional method energetic performance index advanced methods variables, including empirical information related to pollutants emissions. The most relevant result is reduction 13% average...

We present here a comparative study of nonlinear structures for best model identification the dynamic behavior total radiation emitted from flame in combustion process. A successful open up its use as an optical variable correct diagnostic and control. test fit Hammerstein-Weiner structure with polynomial or wavelet nonlinearity, NARMAX sigmoidal nonlinearity.

Flame spectroscopy is extensively used in the analysis of industrial combustion processes. A flame emits energy over a wide spectral region and its associated spectra contains both continuous discontinuous components. In literature there are models, based on either Planck's or Wien's law, for representing behavior under different parameters; however, non-linear nature these high dimension data, superposition between emissions complicate theoretical this paper exploratory data to derive...

The correct identification of minerals is crucial task for the exploration and exploitation mineral resources, environmental monitoring, industrial processes. In this article, we propose a hyperspectral imaging system classification model to identify nine types minerals. To accomplish this, employed shortwave infrared (SWIR) camera capture images. We then introduce convolutional neural network (CNN) architecture that considers only spectral data, complemented by fully connected...

This study proposes a technique to classify and identify different wood species by analyzing the spectral emission during combustion time lapse of samples. Homogeneous samples from four were combusted in temperature-controlled furnace, emitted spectra measured using calibrated spectrophotometer. Discontinuous associated with Na K emissions, while continuous used calculate optical variables such as flame temperature, total radiation, energy. A classification model based on XGBoost algorithm...

In this paper, a detailed comparative performance analysis of the two-color satellite laser ranging (SLR) systems Transportable Integrated Geodetic Observatory (TIGO) is presented. The study based on four years continuous measurement data and comparison two different layouts system. focus lies quantitative precision, range accuracy, production for system layouts. Main findings include significant gain in temporal stability due to removal active elements oscillator, an improvement accuracy by...

This work presents a baseline estimation method in flame spectra based on artificial intelligence structure as neural network, combining robust statistics with multivariate analysis to automatically discriminate measured wavelengths belonging continuous feature for model adaptation, surpassing restriction of measuring target training. The main contributions this paper are: analyze database computing Jolliffe from Principal Components Analysis detecting not correlated most the data...

In this work, we set the foundations for design of a control system based on measurement flame's total radiation from combustion process. The key aspect approach is that flame process can be successfully modeled by means Hammerstein system. static nonlinearity, present in examples studied authors, proves to mild and measurable absolute terms using spectral analysis method. As real life example, describe closed-loop solution applied case ladle furnace preheating Our results show proposed...