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

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

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

Deep Learning Approach for Raman Spectroscopy

Raman spectroscopy is a widely used technique for organic and inorganic chemical material identification. Throughout the last century, major improvements in lasers, spectrometers, detectors, and holographic optical components have uplifted Raman spectroscopy as an effective device for a variety of different applications including fundamental chemical and material research, medical diagnostics, bio-science, in-situ process monitoring and planetary investigations. Undoubtedly, mathematical data analysis has been playing a vital role to speed up the migration of Raman spectroscopy to explore different applications. It supports researchers to customize spectral interpretation and overcome the limitations of the physical components in the Raman instrument. However, large, and complex datasets, interferences from instrumentation noise and sample properties which mask the true features of samples still make Raman spectroscopy as a challenging tool. Deep learning is a powerful machine learning strategy to build exploratory and predictive models from large raw datasets and has gained more attention in chemical research over recent years. This chapter demonstrates the application of deep learning techniques for Raman signal-extraction, feature-learning and modelling complex relationships as a support to researchers to overcome the challenges in Raman based chemical analysis

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

Application of Raman spectroscopy to real-time monitoring of CO2 capture at PACT pilot plant; Part 1: Plant operational data

Process analyzers for in-situ monitoring give advantages over the traditional analytical methods such as their fast response, multi-chemical information from a single measurement unit, minimal errors in sample handing and ability to use for process control. This study discusses the suitability of Raman spectroscopy as a process analytical tool for in-situ monitoring of CO2 capture using aqueous monoethanolamine (MEA) solution by presenting its performance during a 3-day test campaign at PACT pilot plant in Sheffield, UK. Two Raman immersion probes were installed on lean and rich streams for real time measurements. A multivariate regression model was used to determine the CO2 loading. The plant performance is described in detail by comparing the CO2 loading in each solvent stream at different process conditions. The study shows that the predicted CO2 loading recorded an acceptable agreement with the offline measurements. The findings from this study suggest that Raman Spectroscopy has the capability to follow changes in process variables and can be employed for real time monitoring and control of the CO2 capture process. In addition, these predictions can be used to optimize process parameters; to generate data to use as inputs for thermodynamic models, plant design and scale-up scenarios

Monitoring of scale formation in a pneumatic conveying system operating in a metal production plant

Monitoring of scale progression in a pneumatic conveying system operating in a metal production plant was conducted during a five-month long monitoring period. A combination of an acoustic monitoring method and a laser imaging method provided detailed information of gradual scale growth in a test pipe. The results show that the scale grew steadily throughout the test campaign. Periods of increasing and decreasing scale growth rate as well as episodes where the scale was detached from the pipe surface could be identified. Additionally, visual inspection of images obtained by the laser device offered information about the spatial distribution of the scale in a cross section of the test pipe.publishedVersio