Dynamic modelling and control of a pilot plant for post-combustion CO2 capture

in Computer Aided Chemical Engineering (2013)

A dynamic model of a post-combustion pilot capture plant is developed using Aspen Plus Dynamics. An innovative process control strategy is studied for regulating the water balance of the process. A ... [more ▼]

A dynamic model of a post-combustion pilot capture plant is developed using Aspen Plus Dynamics. An innovative process control strategy is studied for regulating the water balance of the process. A washing section where the flue gas from the absorber is washed with cold water is included to the process in order to reduce the emissions of amine to the air. Control of the water balance in the solvent loop is successfully achieved by changing the washing water temperature. In previous publications regarding CO2 capture pilot plants, the regulation of the water balance always required a water make-up flow which appears here as unnecessary. Rejection of disturbances and different load reduction scenarios are tested to confirm the efficiency of this strategy. Potential operational problems of this control strategy are identified and solved. [less ▲]

Synthèse de MWNT dans un réacteur continu incliné rotatif à lit mobile par procédé CCVD

Conference (2012, October)

POST-COMBUSTION CO2 CAPTURE: Global Process Simulation and Solvent Degradation

Poster (2012, February)

One of the biggest upcoming challenges concerning both environmental and energy systems engineering is the control and limitation of greenhouse gas emissions due to human activity. Fossil fuels-fired ... [more ▼]

One of the biggest upcoming challenges concerning both environmental and energy systems engineering is the control and limitation of greenhouse gas emissions due to human activity. Fossil fuels-fired power plants are in this context one of the main contributors due to the large amounts of CO2 emitted. Different technologies have been developed for capturing CO2 from such power plants. This work focuses on post-combustion CO2 capture by reactive absorption into amine solvents like monoethanolamine (MEA). The main drawback of this technology is actually the high energy requirement of the process, especially for solvent regeneration. It is then highly interesting to model the capture process so that optimal operating conditions could be approached by simulation thus reducing the number of expensive experimental tests. Thanks to the simulation, it has been possible to identify the most influent process variables and to optimize their value. It was also possible to study the impact of process modifications on the global capture efficiency. The improvements studied allowed for a reduction by up to 14% of the process exergy consumption. Another major drawback of the post-combustion CO2 capture is solvent degradation, which can be due to thermal as well as oxidative mechanisms. Degradation affects the CO2 capture process since it may cause corrosion, foaming and fouling, possibly inducing a decrease of the solvent efficiency and high additional operating costs due to solvent replacement. In order to study degradation of conventional amine solvents as well as degradation of novel solvents, a degradation test rig has been built at the University of Liège in collaboration with the company Laborelec, member of the GDF SUEZ group. First results show that degradation obtained on this lab installation can be compared to degradation results observed on CO2 capture pilot installation. The final objective of this thesis is to make a link between degradation and simulation. Experimental data obtained on the degradation test rig will be implemented into the existing simulation model so that optimal operating conditions considering both process energy efficiency and solvent degradation can be determined. [less ▲]

Study of 2-Ethanolamine degradation

Conference (2012, January)

One major drawback of the post-combustion CO2 capture using conventional amine solvents is solvent degradation, which can be due to thermal as well as oxidative mechanisms. Degradation affects the CO2 ... [more ▼]

One major drawback of the post-combustion CO2 capture using conventional amine solvents is solvent degradation, which can be due to thermal as well as oxidative mechanisms. Degradation affects the CO2 capture process since it may cause corrosion, foaming and fouling, possibly inducing a decrease of the solvent efficiency and high additional operating costs due to solvent replacement. In order to study degradation of conventional amine solvents as well as degradation of novel solvents, a degradation test rig has been built at the University of Liège in collaboration with the company Laborelec, member of the GDF SUEZ group. Since degradation generally occurs within a few months in real plant conditions, this equipment has been designed to allow the study of different degradation mechanisms under accelerated conditions, at high temperatures (up to 140°C) and high partial pressures in oxygen and CO2 (total pressure up to 20 bar with varying gas composition). An advantage of this degradation test rig is that it can be used in batch as well as in semi-batch mode with constant gas flow. High gas-liquid contact efficiency is also ensured thanks to a mechanical agitation system combined with gas bubbling. For a typical run, 300g of aqueous amine solution (30 wt % MEA in water) is introduced into the reactor and degraded during two weeks. The degraded solutions are then analysed using high pressure liquid chromatography (HPLC) for MEA quantification and gas chromatography with flame ionization detector (GC-FID) for the characterization of degradation products. The gas phase is analysed by Fourier Transform Infra Red spectroscopy. The final objective of this work is to implement the data obtained from experimental results on the degradation test rig into an existing simulation model that has been developed based on an existing pilot plant. [less ▲]

Large-scale synthesis of multi-walled carbon nanotubes in a continuous inclined mobile-bed rotating reactor by the catalytic chemical vapour deposition process using methane as carbon source

in Chemical Engineering Journal (2012)

Multi-walled carbon nanotubes (CNTs) were produced in a continuous inclined mobile-bed rotating reactor by the catalytic chemical vapour deposition of methane on a bimetallic Ni-Mo/MgO catalyst whose ... [more ▼]

Multi-walled carbon nanotubes (CNTs) were produced in a continuous inclined mobile-bed rotating reactor by the catalytic chemical vapour deposition of methane on a bimetallic Ni-Mo/MgO catalyst whose activity remains constant in the course of time. Measurements performed on the continuous reactor were validated to ensure that the installation worked correctly and that measurements were precise enough. The performance of the reactor was simulated using a model based on the chemical reactor engineering approach. Hypotheses of the model were verified, and a kinetic study was performed to obtain a kinetic rate expression and to determine the catalytic activity as a function of time. The purity level of produced CNTs depends on the desired properties of the product, so the operating conditions are linked to the purity level that is required. A minimal purity level corresponds to high carbon production, and a maximal purity level corresponds to high specific productivity. It was shown that operating conditions had to be fixed to reach a given specific productivity or a given carbon production, and the optimized operating conditions leading to those two opposite purity level objectives were established. [less ▲]

Kraft RB : recurrent neural network prediction of steam production

Poster (2011, May 30)

In this study, neural networks approaches are compared for predicting the high pressure (HP) steam flow rate from a Kraft recovery boiler. We apply two types of neural networks: a static multilayer ... [more ▼]

In this study, neural networks approaches are compared for predicting the high pressure (HP) steam flow rate from a Kraft recovery boiler. We apply two types of neural networks: a static multilayer perceptron and a dynamic Elman’s recurrent neural network. Starting from a one-day database of raw process data related to the boiler, the goal is to model and predict the next 12-hours of HP steam flow production from the boiler to the steam turbine. The results illustrate the potential of the dynamic approach in this task. [less ▲]

Supervised learning for a Kraft recovery boiler: a data mining approach with Random Forests.

in Favrat, Daniel; Maréchal, François (Eds.) ECOS 2010 Volume IV (Power plants and Industrial processes) (2011, January 01)

A data mining methodology, the random forests, is applied to predict high pressure steam production from the recovery boiler of a Kraft pulping process. Starting from a large database of raw process data ... [more ▼]

A data mining methodology, the random forests, is applied to predict high pressure steam production from the recovery boiler of a Kraft pulping process. Starting from a large database of raw process data, the goal is to identify the input variables that explain the most significant output variations and to predict the high pressure steam flow. [less ▲]

Experimental procedure and statistical data treatment for the kinetic study of selective hydrodechlorination of 1,2-dichloroethane into ethylene over a Pd-Ag sol–gel catalyst

in Chemical Engineering Journal (2011), 173(3), 801-812

The kinetics of selective hydrodechlorination of 1,2-dichloroethane into ethylene over a Pd- Ag/SiO2 catalyst was studied using an a priori experimental design with five independent variables—temperature ... [more ▼]

The kinetics of selective hydrodechlorination of 1,2-dichloroethane into ethylene over a Pd- Ag/SiO2 catalyst was studied using an a priori experimental design with five independent variables—temperature and partial pressures of 1,2-dichloroethane, hydrogen, ethylene and hydrogen chloride. A Langmuir–Hinshelwood model including two types of active site and the 1,2-dichloroethane adsorption as the rate-determining step was found to fit correctly with experimental data, according to the analysis of variance and the analysis of pondered residuals. The study allowed for catalytic deactivation. The rigorous experimental and statistical approach followed to carry out such a kinetic study is explained in detail. [less ▲]

Recurrent neural network prediction of steam production in a Kraft recovery boiler

in Pistikopoulos, E. N.; Georgiadis, M. C.; Kokossis, A. C. (Eds.) 21st European Symposium on Computer Aided Process Engineering (Part B) (2011)

In this paper, neural networks approaches are compared for predicting the high pressure (HP) steam flow rate from a Kraft recovery boiler. We apply two types of neural networks: a static multilayer ... [more ▼]

In this paper, neural networks approaches are compared for predicting the high pressure (HP) steam flow rate from a Kraft recovery boiler. We apply two types of neural networks: a static multilayer perceptron and a dynamic Elman’s recurrent neural network. Starting from a one-day database of raw process data related to the boiler, the goal is to model and predict the next 12-hours of HP steam flow production from the boiler to the steam turbine. The results illustrate the potential of the dynamic approach in this task. [less ▲]

Supervised learning for a Kraft recovery boiler: a data mining approach with Random Forests.

Conference (2010, June)

A data mining methodology, the random forests, is applied to predict high pressure steam production from the recovery boiler of a Kraft pulping process. Starting from a large database of raw process data ... [more ▼]

A data mining methodology, the random forests, is applied to predict high pressure steam production from the recovery boiler of a Kraft pulping process. Starting from a large database of raw process data, the goal is to identify the input variables that explain the most significant output variations and to predict the high pressure steam flow. [less ▲]