Characterization of the Flow (Breakup) Regimes in a Twin-Fluid Atomizer based on Nozzle Vibrations and Multivariate Analysis

In the study, a new non-intrusive approach based on acoustic chemometrics, which includes vibration signal collection using glued-on accelerometers, was assessed for the classification of the different flow (breakup) regimes spanning a whole range of fluids (water and air) flow rates in this twin-fluid atomizer (one-analyte system). This study aims to determine the flow regimes based on the dimensionless number (B), whose unique values correspond to different flow (breakup) regimes. The principal component analysis (PCA) was employed to visually classify the breakup regimes through cluster formation using score plots. The model prediction performance was studied using PLS-R, RMSEP values show error ranges within acceptable limit when tested on independent data. The present acoustic study can serve as a good alternative to the imaging methods employed for flow classification

Spray drop size characterization in an external-mixing bluff-body atomizer based on acoustics and Multivariate Analysis

Air-assist atomizers have been widely used in various applications such as the aerospace industry, internal combustion engines, molten metal, food processing, etc. The mean drop size for these atomizers was obtained through the Shadowgraph imaging technique. This study aims to assess the feasibility of the acoustic chemometrics approach for classifying the atomizer types and predicting the mean drop size, such as Sauter mean diameter (SMD), for a two-phase spray atomizer employed. The droplet size measurements were carried out at three radial locations and one axial location for various air and liquid (water) flow rates. The acoustic signals were recorded through two different sensors: accelerometers and microphones. The main objective of this work is to implement prediction models for the mean drop sizes (SMD) measured at various locations. The model prediction is based on the dimensionless number B, whose unique values correspond to different two-phase flow working conditions. This analysis will further cater to the question that whether the acoustics chemometrics approach, including Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS-R), is suitable for extracting valuable information such as predicting mean drop size (SMD) in two-phase flows through recorded acoustic signals.publishedVersio

Monitoring of scaling in dilute phase pneumatic conveying systems using non-intrusive acoustic sensors – A feasibility study

Scale formation in pneumatic conveying systems is a major industrial challenge. The underlying scale formation mechanisms can be intricate as they often involve a combination of several mutually enhancing binding forces and can be affected by a number of different factors. A non-intrusive monitoring technique capable of measuring scale growth would be a valuable tool to investigate different scaling mechanisms. In this study, the feasibility of an active acoustic sensor technique for monitoring of scale growth in a pneumatic conveying system is evaluated. Tests are performed in a pilot scale pneumatic conveying system transporting sand in dilute phase. The acoustic sensors conducts measurements on test pipes which are coated with a primer/powder mixture, one layer after the other, to simulate scale progression. Reference measurements of the coating layer thickness in the test pipes are obtained by a laser imaging technique for each added coating layer. A multivariate method is used to calibrate prediction models of the scale thickness using acoustic measurements as independent variables and the reference measurements as the dependent variable. Results show that the active monitoring method is capable of monitoring scale growth in pneumatic conveying systems and that dilute phase conveying of sand does not affect the precision of predictions made by the method.acceptedVersio

Modeling, Identification and Control at Telemark University College

Master studies in process automation started in 1989 at what soon became Telemark University College, and the 20 year anniversary marks the start of our own PhD degree in Process, Energy and Automation Engineering. The paper gives an overview of research activities related to control engineering at Department of Electrical Engineering, Information Technology and Cybernetics

Process Analytical Technology for CO2 Capture

Carbon capture and storage, which is also known as CCS, is an obligatory climate change mitigation technology to reduce the carbon dioxide gas emissions to the atmosphere thus limiting the average global temperature increase to 2°C. Process analytical technology is a scientific tool to improve process qualities and performance through timely measurements. This chapter describes how process analytical technology can be imbedded to a carbon capture technology by giving a detailed example of implementation of a process analyzer to CO2 capture by alkanolamine absorption process. Such an implementation requires success in five elements, which are described in this chapter. They are as follows: selecting an appropriate process analyzer, integration between the analyzer and the process, model development to enable the analyzer to predict a process-related chemical or physical attribute, use of the developed model in real-time application and use of the data obtained from the analyzer as an input to a process control unit. Partial least square regression model is a useful chemometric-based method to extract hidden chemical information in measurements from a process analyzer. In this chapter, four partial least square regression models are presented, which are developed to predict CO2 concentration for four different alkanolamine solutions when these amines are used to absorb CO2 from a combustion process

Response Surface Modelling to Reduce CO2 Capture Solvent Cost by Conversion of OZD to MEA

The increasing CO2 concentration in the atmosphere is the most urgent global challenge. The most mature CO2 abatement option is post-combustion CO2 capture employing Monoethanolamine (MEA) solvent. One challenge of using MEA is its in-service degradation to 2-oxazolidinone (OZD), a heterocyclic five-membered organic ring compound. Furthermore, OZD degrades more MEA leading to CO2 capture solvent loss and hence increased operational cost. It is therefore of interest to investigate methods to convert OZD back to MEA. This work reports the conversion of 2-oxazolidinone to MEA by heat treatment at an alkaline condition. Raman spectroscopy and Ion-Exchange chromatography were applied to qualify and quantify the reaction. The optimal reaction parameters were identified by an experimental design model using the Response Surface Methodology (RSM). A second-order model with three variables and five levels of focus was employed, with the OZD conversion percentage as the response. This methodology was chosen because such a model could estimate the main effects, interactions and quadratic terms by relying on a relatively small number of experiments. 17 experimental runs were designed by the software using this method. At a reaction time of 35 minutes, reaction temperature of 100°C, and 2.5 mole of hydroxide per mole of OZD resulted in a complete conversion of OZD to MEA

Low-Viscosity Nonaqueous Sulfolane-Amine-Methanol Solvent Blend for Reversible CO2 Capture: Part II. Blend Optimization, Water Effect, and Speciation

This work is part II of the investigation of a low viscosity, low regeneration energy nonaqueous CO2 capture solvent blend consisting of readily available chemicals diisopropylamine (DPA)–methanol–sulfolane and featuring the monomethyl carbonate anion (MMC) as a CO2 absorption specie. The region of practical solvent composition, high CO2 capacity, low regeneration energy, and avoidance of solvent solidification at a CO2 capture condition is identified by the design of experiment methodology combined with solvent attenuated total reflectance–Fourier transform infrared and nuclear magnetic resonance monitoring and speciation analysis. Flue gas contains water vapor; hence, solvent water stability is important. The limit of solvent blend water content is determined to be 5 wt % because of MMC decomposition to DPA-bicarbonate and -carbamate.publishedVersio

Demonstration of CO2 Capture Process Monitoring and Solvent Degradation Detection by Chemometrics at the Technology Centre Mongstad CO2 Capture Plant

Solvent management is one of the important current challenges in post combustion carbon capture (PCC) technology development. Using large-scale 1960 h test campaign data (Technology Centre Mongstad, Norway, 2015 MEA Test), we demonstrate a combination of multivariate methods (PLS-R, MSPC) and process analytical spectroscopy (FT-IR) as a tool to monitor and control PCC process performance. Two MEA solvent monitoring models, total inorganic carbon (TIC) content and total alkalinity (TA), were prepared. In long-term solvent monitoring, PLS-R model prediction uncertainty increased due to gradual solvent changes, e.g., solvent degradation and impurity accumulation. Hence, we show a specific model update methodology to keep the models updated, leading to good long-term monitoring ability of the TIC and TA models. In addition to reliable long-term solvent monitoring ability, a new principle for follow-up of thermal solvent reclaiming was demonstrated. This shows that the need for solvent reclaiming can be quantified. Furthermore, this methodology is an indicator to see the actual solvent deviation from the fresh solvent. This quantification may provide an input for “start” and “end of reclaiming operation” identification. Hence, we demonstrate that it is possible to extract information for process performance follow-up, solvent monitoring, and solvent reclaiming from a single spectroscopic instrument.publishedVersio

Low-Viscosity Nonaqueous Sulfolane-Amine-Methanol Solvent Blend for Reversible CO2 Capture

In this work, the absorption–desorption performance of CO2 in six new solvent blends of amine (diisopropylamine (DPA), 2-amino-2-methyl-1-propanol (AMP), methyldiethanolamine (MDEA), diethanolamine (DEA), diisopropanolamine (DIPA), and ethanolamine (MEA)), sulfolane, and methanol has been monitored using ATR-FTIR spectroscopy. Additionally, NMR-based species confirmation and solvent viscosity analysis were done for DPA solvent samples. The identified CO2 capture products are monomethyl carbonate (MMC), carbamate, carbonate, and bicarbonate anions in different ratios. The DPA solvent formed MMC entirely with 0.88 molCO2/molamine capture capacity, 0.48 molCO2/molamine cyclic capacity, and 3.28 mPa·s CO2-loaded solvent viscosity. MEA, DEA, DIPA, and MDEA were shown to produce a low or a negligible amount of MMC while AMP occupied an intermediate position.publishedVersio