A non-reactive model system comprising a highly concentrated and unstable oil-in-water emulsion was used to investigate the retention of oil by the membrane in producing biodiesel with a membrane reactor. Critical flux was identified using the relationship between the permeate flux and transmembrane pressure along with the separation efficiency of the membrane. It was shown that separation efficiencies above 99.5% could be obtained at all operating conditions up to the critical flux. It was observed that the concentration of oil in all collected permeate samples using the oil-water system was below 0.2 wt% when operating at a flux below the critical flux. Studies to date have been limited to the characterization of low concentrated emulsions below 15 vol.%. The average oil droplet size in highly concentrated emulsions was measured as 3200 nm employing direct light scattering (DLS) measurement methods. It was observed that the estimated cake layer thickness of 20 to 80 mm was larger than external diameter of the membrane tube i.e. 6 mm based on a large particle size. Settling of the concentrated emulsion permitted the detection of a smaller particle size distribution (30-100 nm) within the larger particles averaging 3200 nm. It was identified that DLS methods could not efficiently give the droplet size distribution of the oil in the emulsion since large particles interfered with the detection of smaller particles. The content of the smaller particles represented 1% of the total weight of oil at 30°C and 5% at 70°C. This was too low to be detected using DLS measurements but was sufficient to affect ultrafiltration. In order to study the critical flux in the presence of transesterification reaction and the effect of cross flow velocity on separation, various oils were transesterified in another membrane reactor providing higher cross flow velocity. higher cross flow velocity provides better separation by reducing materials deposition on the surface of the membrane due to higher shearing. The oils tested were canola, corn, sunflower and unrefined soy oils (Free Fatty Acids (FFA< 1%)), and waste cooking oil (FFA= 9%). The quality of all biodiesel samples was studied in terms of glycerine, mono-glyceride, di-glyceride and tri-glyceride concentrations. The composition of all biodiesel samples were in the range required by ASTM D6751 and EN 14214 standards. A critical flux based on operating pressure in the reactor was reached for waste cooking and pre-treated corn oils. It was identified that the reaction residence time in the reactor was an extremely important design parameter affecting the operating pressure in the reactor. / Natural Sciences and Engineering Research Council of Canada (NSERC)

Hydrocracking and hydrotreating are bitumen upgrading technologies designed to enhance fuel quality by decreasing its density, viscosity, boiling point and heteroatom content via hydrogen addition. The aim of this thesis is to model and simulate an upgrading and integrated gasification combined cycle then to evaluate the feasibility of integrating slurry hydrocracking, trickle-bed hydrotreating and residue gasification using the Aspen HYSYS® simulation software. The close-coupling of the bitumen upgrading facilities with gasification should lead to a hydrogen, steam and power self-sufficient upgrading facility with CO2 capture. Hydrocracker residue is first withdrawn from a 100,000 BPD Athabasca bitumen upgrading facility, characterized via ultimate analysis and then fed to a gasification unit where it produces hydrogen that is partially recycled to the hydrocracker and hydrotreaters and partially burned for power production in a high hydrogen combined cycle unit. The integrated design is simulated for a base case of 90% carbon capture utilizing a monoethanolamine (MEA) solvent, and compared to 65% and no carbon capture scenarios. The hydrogen production of the gasification process is evaluated in terms of hydrocracker residue and auxiliary petroleum coke feeds. The power production is determined for various carbon capture cases and for an optimal hydrocracking operation. Hence, the feasibility of the integration of the upgrading process and the IGCC resides in meeting the hydrogen demand of the upgrading facility while producing enough steam and electricity for a power and energy self-sufficient operation, regardless of the extent of carbon capture.

A non-reactive model system comprising a highly concentrated and unstable oil-in-water emulsion was used to investigate the retention of oil by the membrane in producing biodiesel with a membrane reactor. Critical flux was identified using the relationship between the permeate flux and transmembrane pressure along with the separation efficiency of the membrane. It was shown that separation efficiencies above 99.5% could be obtained at all operating conditions up to the critical flux. It was observed that the concentration of oil in all collected permeate samples using the oil-water system was below 0.2 wt% when operating at a flux below the critical flux. Studies to date have been limited to the characterization of low concentrated emulsions below 15 vol.%. The average oil droplet size in highly concentrated emulsions was measured as 3200 nm employing direct light scattering (DLS) measurement methods. It was observed that the estimated cake layer thickness of 20 to 80 mm was larger than external diameter of the membrane tube i.e. 6 mm based on a large particle size. Settling of the concentrated emulsion permitted the detection of a smaller particle size distribution (30-100 nm) within the larger particles averaging 3200 nm. It was identified that DLS methods could not efficiently give the droplet size distribution of the oil in the emulsion since large particles interfered with the detection of smaller particles. The content of the smaller particles represented 1% of the total weight of oil at 30°C and 5% at 70°C. This was too low to be detected using DLS measurements but was sufficient to affect ultrafiltration. In order to study the critical flux in the presence of transesterification reaction and the effect of cross flow velocity on separation, various oils were transesterified in another membrane reactor providing higher cross flow velocity. higher cross flow velocity provides better separation by reducing materials deposition on the surface of the membrane due to higher shearing. The oils tested were canola, corn, sunflower and unrefined soy oils (Free Fatty Acids (FFA< 1%)), and waste cooking oil (FFA= 9%). The quality of all biodiesel samples was studied in terms of glycerine, mono-glyceride, di-glyceride and tri-glyceride concentrations. The composition of all biodiesel samples were in the range required by ASTM D6751 and EN 14214 standards. A critical flux based on operating pressure in the reactor was reached for waste cooking and pre-treated corn oils. It was identified that the reaction residence time in the reactor was an extremely important design parameter affecting the operating pressure in the reactor. / Natural Sciences and Engineering Research Council of Canada (NSERC)

S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.