Advancement and new perspectives of using formulated reactive amine blends for post-combustion carbon dioxide (CO_(2)) capture technologies

Chemical absorption using amine-based solvents have proven to be the most studied,as well as the most reliable and efficient technology for capturing carbon dioxide(CO_(2))from exhaust gas streams and synthesis gas in all combustion and industrial processes.The application of single amine-based solvents especially the very reactive monoethanolamine(MEA)is associated with a parasitic energy demand for solvent regeneration.Since regeneration energy accounts for up to threeequarters of the plant operating cost,efforts in its reduction have prompted the idea of using blended amine solvents.This review paper highlights the success achieved in blending amine solvents and the recent and future technologies aimed at increasing the overall volumetric mass transfer coefficient,absorption rate,cyclic capacity and greatly minimizing both degradation and the energy for solvent regeneration.The importance of amine biodegradability(BOD)and low ecotoxicity as well as low amine volatility is also highlighted.Costs and energy penalty indices that influences the capital and operating costs of CO_(2) capture process was also highlighted.A new experimental method for simultaneously estimating amine cost,degradation rate,regeneration energy and reclaiming energy is also proposed in this review paper.

Application of carbon nanotubes prepared from CH_(4)/CO_(2) over Ni/MgO catalysts in CO_(2) capture using a BEA-AMP bi-solvent blend

Carbon nanotubes(CNTs)were synthesized by the chemical vapour deposition of methane and carbon dioxide over a Ni/MgO catalyst.The synthesized CNTs were then mixed with K/MgO catalyst at different ratios and used as the catalyst for CO_(2) absorption in butylethanolamine-2-amino-2-methyl-l-propanol bi-solvent blend.The catalysts were characterized using X-ray diffraction,scanning electron microscopy,butylethanolamine,thermal gravimetric analysis and temperature-programmed desorption of carbon dioxide in order to determine the characteristics responsible for good CO_(2)-absorption performance.The results showed that,with the addition of a catalyst into the amine solution,the amine reached equilibrium CO_(2) loading faster than without a catalyst.Also,the increase in the CNT content of the KMgO/CNTs catalyst made the CO_(2) absorption reach equilibrium much more quickly compared with just KMgO alone and without a catalyst.The KMgO/CNTs at a ratio of 1:4 yielded the fastest time to reach CO_(2)-loading equilibrium at 240 min,which was mainly due to the increase in strong basic sites as well as the highest total basic sites with an increase in CNT content.In addition,because of the extremely large specific surface area and pore volume generated due to the CNT,the number of exposed active centres per unit mass increased tremendously,leading to a corresponding tremendous increase in CO_(2) absorption.

The history and development of the IEA GHG Weyburn-Midale CO_(2) Monitoring and Storage Project in Saskatchewan, Canada (the world largest CO_(2) for EOR and CCS program)

1.Introduction The Weyburn oil field occurs within a larger trend of similar oil accumulations in Mississippian-aged carbonates.The history of discovery of these oil accumulations resulted in the fields being given several different names.The Weyburn field and the immediately-adjacent Midale field are effectively part of a larger oil pool with OOIP(original oil in place)reserves of over 2 billion barrels.This is certainly a large conventional oil pool for Canada even if not large by world standards.These pools are found in truncated stratigraphic traps and occur at about 1450e1550 m depth making them ideal for CO_(2) storage[1].

Recent progress and new developments of applications of artificial intelligence (AI), knowledge-based systems (KBS), and Machine Learning (ML) in the petroleum industry

Over the past two decades,broad-based and intense efforts have been devoted to apply concepts and methodologies from information technology(IT)and artificial intelligence(AI)to informatics research related to energy and environmental systems.Both academic research and industrial practices have generated an impressive amount of literature,which spans virtually every aspect of synergistic work among these disciplines.Informatics and systems analysis techniques are now being widely used by the practicing engineers and scientists to solve a broad range of problems in the petroleum industry.Speaking at the World Economic Forum at Davos Switzerland in 2017,Ginni Rometty,IBM′CEO at that time(now Executive Chairperson of IBM),described AI's role in the partnership between humans and machines as“augmented cognition.”In other words,AI not only supports but augments human cognition so that humans can be more efficient and“do a better job”[1,2].This should also be true and very beneficial for the petroleum industry.

Mass-transfer studies of solid-base catalyst-aided CO_(2) absorption and solid-acid catalyst-aided CO_(2) desorption for CO_(2) capture in a pilot plant using aqueous solutions of MEA and blends of MEA-MDEA and BEA-AMP

Mass-transfer studies of catalyst-aided CO_(2) absorption and desorption were performed in a full-cycle,bench-scale pilot plant to improve CO_(2) absorption using 5M MEA,5M MEA-2M MDEA and 2M BEA-2M AMP.A solid-base catalyst,K/MgO,and an acid catalyst,HZSM-5,were used to facilitate absorption and desorption,respectively.Absorption and desorption mass-transfer performance was presented in terms of the overall mass-transfer coefficient of the gas side(KGav)and liquid side(K_(L)a_(v)),respectively.For non-catalytic runs,the highest K_(G)a_(V) and K_(L)a_(V) were 0.086 Kmol m^(3).kPa.hr and 0.7851 hr for 2M BEA-2M AMP solvent.The results showed 38.7% KGav and 23.6% K_(L)a_(v) increase for 2M BEA-2M AMP with only HZSM-5 catalyst in desorber and a 95% K_(G)a_(V) and 45% K_(L)a_(V) increase for both K/MgO catalyst and HZSM-5 catalyst.This was attributed to the role of K/MgO in bonding loosely with CO_(2) and making it available for the amine reaction.

Predictions of equilibrium solubility and mass transfer coefficient for CO_(2) absorption into aqueous solutions of 4-diethylamino-2-butanol using artificial neural networks

In the present work,artificial neuron network(ANN)based models for predicting equilibrium solubility and mass transfer coefficient of CO_(2) absorption into aqueous solutions of high performance alternative 4-diethylamino-2-butanol(DEAB)solvent were successfully developed.The ANN models show an outstanding predictive performance over the predictive correlations proposed in the literature.In order to predict the equilibrium solubility,the ANN model were developed based on three input parameters of operating temperature,concentration of DEAB and partial pressure of CO_(2).An outstanding prediction performance of 2.4%average absolute deviation(AAD)can be obtained(comparing with 7.1–8.3%AAD from the literature).Additionally,a significant improvement on predicting mass transfer coefficient can also be achieved through the developed ANN model with 3.1%AAD(comparing with 14.5%AAD from the existing semi-empirical model).The mass transfer coefficient is considered to be a function of liquid flow rate,liquid inlet temperature,concentration of DEAB,inlet CO_(2) loading,outlet CO_(2) loading,concentration of CO_(2) along the height of the column.