Investments in carbon capture from industrial emissions have been on the rise in recent years, having reached over $200 million in 2021 as compared to 2015’s $13 million. The Paris Agreement, signed by 196 parties globally in 2015, is purported to be the primary driver for this, with its ambitious goal of limiting global surface temperature rise to 1.5°C by the year 2100 as compared to the pre-industrial era. Achievement of a carbon-neutral future for industries has been sought by experts in more than a few ways, which include attempts directed towards re-designing current manufacturing processes to produce inherently low CO2 emissions. Although eventual elimination of carbon emissions forms the ultimate goal, complete avoidance of CO2 production does not seem probable for all industrial sectors. Emissions from industries in the medium to long term are thus foreseen to be composed between 0.5% and 7% of CO2 by moles (roughly between 1% and 10% by mass), depending on the level of dilution occurring during the various flue gas treatment procedures between their source and the capture unit. An assessment of the capabilities of two popular and one prospective carbon capture technologies in capturing CO2 from such emissions of the future has been made in this work to aid investors make informed decisions about a suitable technology. The monoethanolamine-based (MEA) absorption system, one of the most popular choices today, was found to be well capable of treating emissions composed of CO2 in proportions as low as 0.6% by mole (or ∼1% by mass) with capture rates well over 95%. Its thermal energy intensity ranged between 3.59 MJth/kgCO2 captured and 10.23 MJth/kgCO2 captured with an associated levelised cost of capture ranging between €20.36/tonneCO2 captured and €141.97/tonneCO2 captured going from the 10% concentrated to the 1% concentrated stream by mass. In comparison, the benfield system was found to effect much lower CO2 capture rates ranging between 35% and 88%, making it unsuitable for treatment of low CO2 concentrated streams. Even with such poor performance at high pressures of operation, its energy demand ranged between 3.9 MJth/kgCO2 captured and 11.07 MJth/kgCO2 captured with an associated levelised cost of capture between €174.28/tonneCO2 captured and €4209.06/tonneCO2 captured. The immobilised amine-based system, in what is considered to be a non-optimised configuration yet, was found to capture nearly 100% of the entering CO2 with energy consumption ranging between 3.71MJth/kgCO2 captured and 11.8 MJth/kgCO2 captured for extremely high, but improvable levelised costs of capture ranging between €674.31/tonneCO2 captured and €3488.42/tonneCO2 captured. Exhibiting comparable energy performance to the mature MEA-based absorption system’s even in its non-optimised configuration, the immobilised amine-based adsorption system was found to possess potential to be the carbon capture technology of the future for treatment of low CO2-concentrated effluent streams. / Investeringar i koldioxidavskiljning från industriella utsläpp har ökat de senaste åren och nått över 200 miljoner USD 2021 jämfört med 2015 års 13 miljoner USD. Parisavtalet, som undertecknades av 196 parter globalt 2015, påstås vara den främsta drivkraften för detta, med det ambitiösa målet att begränsa den globala yttemperaturökningen till 1,5°C till år 2100 jämfört med den förindustriella eran. Att uppnå en koldioxidneutral framtid för industrier har eftersträvats av experter på mer än ett fåtal sätt, vilket inkluderar försök inriktade på att omdesigna nuvarande tillverkningsprocesser för att producera låga CO2-utsläpp. Även om fullständig eliminering av koldioxidutsläpp utgör det ideala målet, är det inte troligt att CO2-produktion kan undvikas helt för att alla industrisektorer. Utsläppen från industrier på medellång till lång sikt förväntas därför utgöra mellan 0,5 % och 7 % av CO2 per mol (ungefär mellan 1 % och 10 % i massa), beroende på nivån av utspädning som inträffar under de olika rökgasbehandlingsprocedurerna mellan utsläppskällan och fångstenheten. I det här arbetet har två traditionella och en potentiellt blivande koldioxidavskiljningsteknik jämförts och en bedömning av deras förmåga att fånga in CO2 från framtida utsläpp har gjorts i syfte att hjälpa investerare att göra ett klokt val. Det monoetanolaminbaserade (MEA) absorptionssystemet, ett av de mest populära valen idag, visade sig vara väl kapabelt att behandla utsläpp med CO2-koncentrationer så låga som 0,6 molprocent (eller 1 massprocent) med fångsthastigheter långt över 95 %. Dess termiska energiintensitet varierade mellan 3,59 MJth/kgCO2 captured och 10,23 MJth/kgCO2 captured med en tillhörande utjämnad kostnad för fångst mellan €20,36/tonCO2 captured och €141,97/tonCO2 captured från 10 % koncentrerad till 1 % koncentrerad ström i massa. Som jämförelse visade sig benfield-systemet ge mycket lägre CO2-avskiljningshastigheter på mellan 35 % och 88 %, vilket gör metodenolämplig för behandling av gasströmmar med låg CO2-koncentration. Den dåliga prestandan uppvisades trots höga drifttryck och metoden medförde en energiintensitet mellan 3,9MJth/kgCO2 captured till 11,07MJth/kgCO2 captured samt en snittkostnad mellan €174/tonCO2 captured till €4209,06/tonCO2 captured. Det immobiliserade aminbaserade systemet anses vara en icke-optimerad konfiguration men visade sig trots det fånga upp nästan 100 % av inkommande CO2 med en energiförbrukning på mellan 3,71 MJth/kgCO2 captured och 11,8 MJth/kgCO2 captured. De extremt höga, men dock förbättringsbara, snittkostnaderna för infångningen sträcker sig mellan €674/tonCO2 captured och €3488,42/tonCO2 captured. Det immobiliserade aminbaserade adsorptionssystemet uppvisar jämförbar energiprestanda som det mogna MEA-baserade absorptionssystemet även i sin icke-optimerade konfiguration.

Closed-loop system identification and fault detection and isolation are the two fundamental building blocks of process monitoring. Efficient and accurate process monitoring increases plant availability and utilisation. This dissertation investigates a subspace system identification and fault detection methodology for the Benfield process, used by Sasol, Synfuels in Secunda, South Africa, to remove CO2 from CO2-rich tail gas. Subspace identification methods originated between system theory, geometry and numerical linear algebra which makes it a computationally efficient tool to estimate system parameters. Subspace identification methods are classified as Black-Box identification techniques, where it does not rely on a-priori process information and estimates the process model structure and order automatically. Typical subspace identification algorithms use non-parsimonious model formulation, with extra terms in the model that appear to be non-causal (stochastic noise components). These extra terms are included to conveniently perform subspace projection, but are the cause for inflated variance in the estimates, and partially responsible for the loss of closed-loop identifiably. The subspace identification methodology proposed in this dissertation incorporates two successive LQ decompositions to remove stochastic components and obtain state-space models of the plant respectively. The stability of the identified plant is further guaranteed by using the shift invariant property of the extended observability matrix by appending the shifted extended observability matrix by a block of zeros. It is shown that the spectral radius of the identified system matrices all lies within a unit boundary, when the system matrices are derived from the newly appended extended observability matrix. The proposed subspace identification methodology is validated and verified by re-identifying the Benfield process operating in closed-loop, with an RMPCT controller, using measured closed-loop process data. Models that have been identified from data measured from the Benfield process operating in closed-loop with an RMPCT controller produced validation data fits of 65% and higher. From residual analysis results, it was concluded that the proposed subspace identification method produce models that are accurate in predicting future outputs and represent a wide variety of process inputs. A parametric fault detection methodology is proposed that monitors the estimated system parameters as identified from the subspace identification methodology. The fault detection methodology is based on the monitoring of parameter discrepancies, where sporadic parameter deviations will be detected as faults. Extended Kalman filter theory is implemented to estimate system parameters, instead of system states, as new process data becomes readily available. The extended Kalman filter needs accurate initial parameter estimates and is thus periodically updated by the subspace identification methodology, as a new set of more accurate parameters have been identified. The proposed fault detection methodology is validated and verified by monitoring process behaviour of the Benfield process. Faults that were monitored for, and detected include foaming, flooding and sensor faults. Initial process parameters as identified from the subspace method can be tracked efficiently by using an extended Kalman filter. This enables the fault detection methodology to identify process parameter deviations, with a process parameter deviation sensitivity of 2% or higher. This means that a 2% parameter deviation will be detected which greatly enhances the fault detection efficiency and sensitivity. / Dissertation (MEng)--University of Pretoria, 2008. / Electrical, Electronic and Computer Engineering / unrestricted