[{"content":" Overview # Conventional alternatives to fossil transportation fuels often require sweeping infrastructure changes and new vehicle development. Drop-in renewable fuels like biodiesel sidestep these barriers by working with existing diesel engines and distribution networks.\nThis project presents a techno-economic analysis of a fully renewable biodiesel production supply chain sourced from waste cooking oil (WCO), targeting an output of 980 kg/h of biodiesel. With roughly 3 billion gallons of WCO generated annually in the US, most of which is disposed, there is a compelling feedstock opportunity.\nThe central design decision was to integrate enzymatic and supercritical transesterification into a single pipeline, leveraging the strengths of each method while compensating for their individual weaknesses.\nProcess Design # Transesterification: Two Methods, One Pipeline # Property Acid Catalyzed Base Catalyzed Supercritical Enzymatic Temperature 1 atm, 25°C Low High (290–340°C) 40–50°C Yield Low High Fast-decent yields Fast-decent yields Key Advantage — Low cost No catalyst needed Enzymes are reusable Key Drawback High alcohol/oil ratio, corrosion Pre-treatment required, difficult recovery High pressure, high cost Slow reaction, enzyme separation required Why Ethanol Over Methanol? # Property 95% Ethanol (C₂H₅OH) Methanol (CH₃OH) Flash Point −15.5°C (59.9°F) −11°C (52°F) OSHA PEL 1,000 ppm 200 ppm Toxicity Low High Environmental Impact Biodegradable Toxic to aquatic life Ethanol was selected as the transesterification alcohol for its significantly lower toxicity, better environmental profile, and compatibility with the enzymatic process.\nReaction Chemistry # The transesterification reactions proceed stepwise:\nTriglyceride + Ethanol → Ethyl Esters + Diglyceride Diglyceride + Ethanol → Ethyl Esters + Monoglyceride Monoglyceride + Ethanol → Ethyl Esters + Glycerol Free Fatty Acids + Ethanol → Ethyl Esters + Water Enzymatic Reactor # The enzymatic stage uses Thermomyces Lanuginosus Lipase immobilized on magnetic beads.\nEnzyme Properties:\nCompatible with ethanol solvent Cost: $30/kg Reusable for up to 9 cycles Operating temperature: 40–50°C Enzyme/WCO mass ratio: 3% Reactor Summary:\nParameter Value Vessel volume 180 gal Max mixture volume 150 gal Cost per reactor $6,000 Number of reactors 70 Total reactor cost $420,000 Enzyme mass per batch 12.5 kg Avg enzyme cost per batch $53 Total enzyme cost per year $39,000 Kinetics were modeled in MATLAB using Michaelis–Menten kinetic modeling. Enzyme recovery between batches is achieved via neodymium magnet separation with multi-cycle draining to prevent bead blockages.\nSupercritical (PFR) Reactor # At supercritical conditions, ethanol and oil form a single homogeneous phase, eliminating mass transfer limitations and enabling rapid conversion without a catalyst.\nInlet Conditions:\nMass flow: 1,313 kg/h Molar flow: 9.97 kmol/h Temperature: 290°C Pressure: 200 bar Re = 50,000 Reactor Geometry:\nTotal length: 40 m (13 tubes × 3 m each) Tube diameter: 0.1 m Material: Stainless Steel 316L Kinetic Data (First-Order Model, 48 Reactions):\nFatty Acid Rate Constant k at 290°C (1/s) Activation Energy (kJ/mol) C16:0 Palmitic 0.00775 170.84 C18:0 Stearic 0.0097 120.7 C18:1 Oleic 0.0107 92.6 C18:2 Linoleic 0.0128 143.11 C18:3 Linolenic 0.0146 153.4 Aspen Plus Simulation # The central processing facility was modeled in Aspen Plus using a multi-method thermodynamic approach:\nProperty Method Application Peng-Robinson with Boston-Mathias (PR-BM) Supercritical phase UNIFAC-LL Liquid-liquid interactions NRTL Base method The \u0026ldquo;Aqueous Recovery\u0026rdquo; flash drum served as the simulation anchor point, with operating temperature varied to maximize profit. Key design spec targets included FAEE \u0026gt; 96.5%, glycerol \u0026lt; 0.025%, and ethanol \u0026lt; 0.020% in the biodiesel product.\nSupply Chain # WCO is collected across six geographic zones on a rotating schedule and transported to the central processing facility. The collection schedule staggers enzyme deposits and returns across zones (Mon–Sun), ensuring a continuous feedstock supply.\nCollection Fleet (6 Trucks):\nParameter Value Fuel usage 6.5 mil/gal Average speed 35 mph Driver pay $60,000/yr Hours driven/day 8 Feedstock Economics:\nMaterial Cost ($/gal) WCO (market) $0.50 WCO (after adding) $0.25 WCO (new price) $0.75 Product Specifications # Biodiesel — EN14214 Standard (Mass Basis):\nComponent Specification FAME \u0026gt; 96.5% Water \u0026lt; 500 ppm Ethanol \u0026lt; 0.20% Glycerol \u0026lt; 0.25% Glycerol — SRS International Standard (Mass Basis):\nComponent Specification Glycerin 40–88% Water \u0026lt; 12% Organic Residue \u0026lt; 2.00% pH 4.0–9.0 Economic Analysis # Capital \u0026amp; Manufacturing Costs # Cost Category Value ($1k/yr) Fixed Capital Investment 5,600 Working Capital + Startup 480 Land 500 Total Capital 6,580 Cost Category Value ($1k/yr) Raw Materials (WCO, EtOH, Enzymes) 4,090 Utilities 720 Fixed Operating Costs 6,400 Total Cost of Production 10,400 Revenue:\nBiodiesel: 2,906,400 gal/yr × $4.04/gal = $11,700k/yr Glycerol: 201,600 gal/yr × $2.00/gal = $400k/yr Gross Profit: ~$1,700k/yr Project Scenarios (im = 0.15) # Metric Base Case Optimistic Pessimistic NPW $4.0M $6.4M $1.9M EAW $680k $1.0M $330k DCFRR 0.22 0.28 0.16 ROI 0.26 0.30 0.19 BCR 0.75 1.00 0.56 Payback Period 4 years 3 years 4 years Safety \u0026amp; Environmental Considerations # Safety # Hazard Applicable Codes Fire \u0026amp; Explosion (EtOH vapor) NFPA 1, 30, 72, 2001, 15 High Temperature (290–340°C) — High Pressure (200 bar) — Chemical Handling \u0026amp; Storage — Personnel Safety \u0026amp; Training OSHA 1910 H\u0026amp;I, ANSI Z117.1 Key design features include pressure relief valves on all flash drums, the PFR, decanter, and storage tanks; feedback-controlled booster pumps; bypass lines around heat exchangers; and system-wide integration with a venting system to prevent flammability hazards.\nLife Cycle Assessment # Scope: 1 kg of biodiesel from WCO | Method: Recipe 2016 Endpoint (H)\nElectricity had the largest environmental impact (global warming, toxicity, radiation, ecotoxicity) Waste Cooking Oil was the second largest contributor (ozone depletion, eutrophication) Ethanol and enzymes had minimal impacts overall Switching to cleaner electricity sources would substantially reduce the overall environmental burden Conclusions \u0026amp; Recommendations # Conclusions # A fully renewable biodiesel production system, independent of fossil-derived chemicals, is technically feasible. Integrating enzymatic and supercritical processes into one pipeline enables high biodiesel conversion. Economic viability depends heavily on government subsidies for renewable fuels. Further experimental work is needed to obtain ethanol transesterification kinetic data and a more complete P\u0026amp;ID-based economic model. Recommendations # Investigate methanol as an alternative alcohol for the supercritical process. Explore a standalone supercritical ethanol process with no enzymatic stage. Engage auto manufacturers on developing engines optimized for pure biodiesel combustion. ","date":"23 April 2025","externalUrl":null,"permalink":"/projects/capstone-biodiesel/","section":"Projects","summary":"Senior Capstone Design Project and Research Topic","title":"Sustainable Supply Chain Biodiesel Production from Waste Cooking Oil","type":"projects"},{"content":" Background # Anaerobic digesters are a crucial element of biosolids processing in modern wastewater treatment facilities. Biosolids are valuable as organic material, with the caveat that they should fall under a threshold volatile solids content. Anaerobic digestion achieves this \u0026ldquo;volatile solids reduction\u0026rdquo; and produces renewable energy as a byproduct, making it very attractive for wastewater treatment plants. These plants primarily exist as a public service to uphold a sanitation standard, but this introduces a profit incentive.\nThe biosolids processing section of a wastewater treatment facility starts with a preliminary separation of the wastewater feed by density followed by several processes which serve to reduce the liquid content of the biosolids stream, at this point it\u0026rsquo;s referred to as \u0026ldquo;sludge\u0026rdquo;. This stream is then fed into what is essentially a continuously-stirred tank reactor with a mix of micro-organisms inside which perform a multi-stage biological mechanism converting biodegradable organic matter into ultimately carbon-dioxide and methane gas. The tank is large enough such that the residence time is roughly in the order of ~2 weeks. To maintain anaerobic conditions the surface of the liquid is under a floating cover.\nMy Task # Stantec was tasked with assisting with the design and startup of an anaerobic digester cluster that would be able to process the sludge from all of the wastewater treatment plants collectively in the area. When I was brought onto the team, they asked me to help specifically with projecting the feedrates to anaerobic digesters during the startup period.\nSpecifically, what this involves is taking a \u0026ldquo;seed\u0026rdquo; population of methanogens and trying to gradually ramp up the sludge feed such that in a 21-day rampup it will be able to handle a continuous feed of sludge at plant-scale operational flows. A crucial detail is that this is done by intermittently feeding sludge once a day to the \u0026ldquo;seed\u0026rdquo; reactors.\nThe Problem # Anaerobic digestion is a biological process. Biological processes are dynamic and transient in nature. When the micro-organisms get \u0026ldquo;food\u0026rdquo;, they thrive and the population grows, as \u0026ldquo;food\u0026rdquo; grows scarce, the population decreases. However, it\u0026rsquo;s not this simple either. Too much food can lead to a population \u0026ldquo;shock\u0026rdquo; where the microbial population is imbalanced between different biological species, subsequently resulting in resource competition in the future that leads to sharp population decline. Additionally, the extent of the reaction (in industry, measured as the \u0026ldquo;volatile solids reduction\u0026rdquo;) depends on size of the microbial population as well as operational conditions.\nAdditionally, anaerobic digestion produces gas. This is a positive thing given the fact that the methane gas can be refined into renewable natural gas, however, from an operational standpoint, gas production can result in foaming under certain operating conditions. This can induce turbulance and result in reducing the lifespan of equipment.\nFinally, due to the reduced water content of the sludge, it has the complex rheological properties of a non-newtonian fluid. For the most part this is a smaller concern, however there are situations where it becomes crucial such as in how it can produce overflow events during anaerobic digestion. See my explanation on how I determined the containment volume for an anaerobic digester overflow event for more details.\nSo, to translate the technical requirements of a solution to my problem, I need to be able to:\nGradually increase the flowrate to the \u0026ldquo;seed\u0026rdquo; reactors each day such that by the end, the population is capable of handling the full process flow. Avoid any large deviations in the flowrate in order to not produce \u0026ldquo;shocks\u0026rdquo; to the population while still promoting growth. Model the volatile solids reduction of the process based on the projected change in microbial population to validate that gas production is within the appropriate operating range. My Approach # To ensure that I was able to get a solution which would be an accurate representation of the real world, there were a few key questions I needed to be able to answer:\nWhat is the safe margin for day-by-day increases to sludge feeds on an intermittent basis? What ratio of incoming solids to volatile solids reduction produces operational strain from foaming? How does the volatile solids reduction of a population evolve over time? To do this, I collaborated with the lab to get experimental data. I understood that there were limitations to how well the data would encapsulate the much larger scale processes, but I would be collaborating to refine these over time as data comes in during the startup process.\nA \u0026ldquo;safe margin\u0026rdquo; for an increase in the daily feed rate constitutes a percentage value which resulting in minimal variance in the volatile solids reduction value when fit to a sigmoid curve. What the \u0026ldquo;safe-margin\u0026rdquo; is really trying to enforce is a gradual population change which is sufficiently rapid but won\u0026rsquo;t induce \u0026ldquo;shock\u0026rdquo;. Because volatile solids reduction is largely tied to microbial population when operating conditions are regulated, it can essentially be used as a \u0026ldquo;population proxy\u0026rdquo; value. A sigmoid curve was used given the fact that it can approximate how population changes occur. Therefore, by fitting a model to the change in volatile solids reduction, the concern about how population evolves over time is addressed as well.\nFoam generation is related to when the growth rate of acid-forming bacteria (responsible for the initial step of the biological process) outpaces the growth of methanogens (micro-organisms responsible for performing the final step). This assymetry results in the accumulation of chemical species which reduce surface tension and stabilize bubbles, preventing generated gas from escaping. This means by regulating the amount of solids being fed relative to the volatile solids reduction, foaming can be mitigated. Using the data from the lab, I obtained the design constraint.\nAfter understanding the constraints of the solution and having a model for volatile solids reduction, I was ready to find a solution. What I needed to do what determine a way of solving sequential mass balances where the input from one day is dependent on the output of another and I could validate that the results met the design specifications at each step.\nTo do this, I opted for a combination of an Excel spreadsheet for mass-balance calculations and microbial growth modeling along with a VBA script to drive a \u0026ldquo;guess-and-check\u0026rdquo; algorithm that would iterate through flowrates until it found one that satisfied all constraints of the system each day. Given that the feed flowrate was limited in the extent that it could be granular (precision was limited to increments of 5,000 lbs/day), the logic for the VBA script did not need to be very complicated.\nThe Result # The initial solution had some limitations. As I mentioned previously, the models I used for this solution were based on smaller scale lab testing which may not accurately encapsulate the dynamics of a plant-scale reactor. However, this \u0026ldquo;initial guess\u0026rdquo; was able to mitigate the majority of operational problems which was an improvement on previous iterations. Additionally, after a week, I was able to use the operational data to regress more accurate models to encapsulate the process, and after this point, nearly all operational problems related to foam generation were mitigated.\nThis was a very ambitious technical problem to have to solve so early in my career and I\u0026rsquo;m glad I was able to contribute something meaningful and overcome the obstacles presented before me. I also developed a fondness for biologically-driven reactions as a result of this which has taken me on a path to learn about protein manufacturing processes and enzymatic reactions. Ideally, in the future I can get an opportunity to creative solutions to other technical problems that similarly have a positive impact on real operation.\n","date":"5 July 2022","externalUrl":null,"permalink":"/projects/anaerobic-startup/","section":"Projects","summary":"Stantec Internship Project","title":"Startup of an Anaerobic Digester Cluster at a Wastewater Treatment Facility","type":"projects"},{"content":" The Problem: A Binary Azeotrope # Methanol and toluene form a binary azeotrope, making high-purity separation significantly more difficult than a standard distillation. At 1.1 bar, the azeotrope forms at a methanol mole fraction of 0.83 — the point where liquid and vapor compositions become equal and the system behaves as if it has reached total equilibrium. Past that point, the phase envelope is very narrow, and conventional methods like a flash drum simply cannot push purity any further.\nx-y diagram of methanol at 1.1 bar Separation Strategy: Extractive Distillation with a Heavy Entrainer # The solution is extractive distillation, which introduces a third component — an entrainer — to break the azeotrope and improve the relative volatility between methanol and toluene. We focused on heavy entrainers, which have a higher boiling point than both feed components. A heavy entrainer preferentially associates with toluene, effectively pulling it away from methanol and allowing clean separation in the distillate.\nThe candidates we evaluated were: aniline, o-xylene, propyl propanoate, methyl butyrate, propyl butyrate, butyl butyrate, and triethylamine.\nWith aniline added, the pseudo-binary x-y diagram shows the azeotrope disappearing entirely and the phase envelope widening substantially at high methanol concentrations — meaning we can push methanol purity well past the 0.83 barrier.\nx-y diagram of methanol in a pseudo-binary system with the addition of aniline at 1.1 bar The overall process uses two extractive distillation columns in series. The first column receives both the feed mixture and the entrainer, separates methanol overhead, and passes the toluene-aniline bottoms to the second column, where toluene and entrainer are split. Both feed and entrainer streams enter at 1.1 bar and 25°C, with condenser pressures at 1 atm.\nAspen Plus flowsheet for the extractive distillation process Simulation: Aspen Plus # The process was modeled in Aspen Plus using the UNIFAC activity coefficient model, selected for its close match to experimental VLE data at these conditions without requiring additional fitted parameters. Both columns used the RadFrac model with equilibrium-stage calculations, which simplifies the system by assuming each stage reaches equilibrium and removes the need for mass transfer coefficients.\nEntrainer Selection # A preliminary 25-stage design was first built to screen entrainers without the stage count constraining results. After testing all candidates, three met the process specifications: o-xylene, propyl butyrate, and butyl butyrate. We then reduced the column to a more realistic 15 stages and re-optimized — at which point aniline emerged as a viable option at a lower entrainer feed rate of 1.55 kmol/hr, and was selected as the final entrainer.\nComparison of simulation results from preliminary design Column Optimization # With the entrainer fixed, each column\u0026rsquo;s distillate rate and reflux ratio were optimized using a iterative \u0026ldquo;hot and cold\u0026rdquo; approach: vary one parameter until the methanol (or toluene) fraction peaks, then optimize the others, and repeat until no further improvement is found. Aspen\u0026rsquo;s built-in sensitivity analysis was used to sweep the reflux ratio and identify the point where the distillate fraction peaked just above specification.\nColumn 1 (Methanol separation):\n15 stages, feed at stage 14, entrainer at stage 3 Distillate rate: 5.51 kmol/hr Optimized reflux ratio: 0.50 Methanol yield: 99.24% Column 2 (Toluene/aniline separation):\n15 stages, feed at stage 14 Distillate rate: 4.51 kmol/hr Optimized reflux ratio: 4.5 Toluene yield: 98.81% Sensitivity Analysis of Column 1 Aniline Flowrate and Methanol Distillate Mol Fraction Sensitivity Analysis of Column 1 Reflux Ratio and Methanol Distillate Mol Fraction Sensitivity Analysis of Column 2 Reflux Ratio and Toluene Distillate Mol Fraction Results # Aspect Achieved Specification Methanol purity 0.9906 ≥ 0.99 Methanol yield 99.24% ≥ 94% Toluene purity 0.9859 ≥ 0.90 Toluene yield 98.81% ≥ 80% The two-column design exceeded all four specifications. The heavy entrainer choice proved especially well-suited for this system: aniline\u0026rsquo;s high boiling point and preferential intermolecular interactions with toluene allow it to effectively pull toluene from the methanol, leaving methanol as the easiest component to recover overhead at high purity. In the second column, aniline and toluene have no azeotrope of their own, so their separation is straightforward with a single column.\nAspen Simulation Results Discussion # The simulation shows strong recovery for both products with minimal material loss. One observation is that the second column considerably overshoots the toluene specification — which, while not a problem from a purity standpoint, does suggest there\u0026rsquo;s room to reduce energy and capital costs by backing off the reflux ratio. In a real industrial process, that over-purification would come at a cost that may not be justified.\nThe most natural improvement would be to recycle the aniline-rich bottoms from the second column back into the entrainer feed, reducing raw material consumption. Further optimization of the second column\u0026rsquo;s operating conditions to bring it closer to spec (rather than well above it) would also improve the overall economics of the design.\n","date":"15 April 2024","externalUrl":null,"permalink":"/projects/azeotropic-distillation/","section":"Projects","summary":"Course Project for Separation Processes","title":"Azeotropic Distillation of Methanol and Toluene","type":"projects"},{"content":" Overview # The goal of this project was to develop and test a multiloop control strategy to maximize profitability and ensure the safe operation of a naphtha cracking plant. Profit is maximized by keeping the reactor temperature as close as possible to its optimal setpoint, while safety is maintained by keeping excess oxygen in the furnace above 9% at all times — dropping below that threshold triggers an alarm and shuts the plant down.\nThe full set of control objectives breaks down into three categories:\nEconomic: Minimize deviations in reactor temperature from the optimal setpoint.\nSafety: Maintain excess O₂ in the furnace above 9% at all times.\nRegulatory:\nHold reactor temperature constant Regulate excess oxygen levels Reject disturbances in fuel and air flow caused by fluctuations in supply pressure Reject disturbances from ambient temperature changes and fuel composition variations Strategy # Since we have two controlled variables — reactor temperature and excess oxygen — we designed two independent control loops, one for each. The approach was:\nIsolate time windows where measurable disturbances (primarily ambient temperature) were constant, then perform step changes to obtain preliminary tuning parameters. Manually adjust those parameters until we found the combination that yielded the highest profit while satisfying all constraints. Test different control strategies (feedback, feedforward, cascade, ratio) and select the combination that best met all objectives. Control Loop 1: Reactor Temperature # After testing several approaches, a feedback + feedforward combination proved to be the best strategy for holding reactor temperature constant at 800°F.\nThe feedback PID controller reads the reactor temperature and compares it to the 800°F setpoint. The feedforward lead-lag controller reads ambient temperature and compares it to a 70°F baseline, preemptively compensating for disturbances before they reach the reactor. The outputs of both controllers are summed and sent to the fuel valve. Hardware: 2 transmitters, 2 controllers, 1 control valve.\n(Insert reactor temperature control schematic)\nControl Loop 2: Excess Oxygen # For excess O₂, the objective is to stay above 9% while keeping the setpoint as low as possible — a lower setpoint means less excess air burned, which means more profit. The best strategy we found was a cascade ratio control scheme.\nInner loop: PID flow controller on the air flow. Outer loop: Variable ratio controller between fuel and air flow, with a PID controller on the excess oxygen that adjusts the ratio setpoint based on measured O₂ levels. Hardware: 3 transmitters, 2 controllers, 1 control valve.\n(Insert excess O₂ control schematic)\nSimulink Implementation # The full control strategy was implemented in MATLAB Simulink. The fuel valve control loop sits at the top of the model and the air valve control loop at the bottom, with both interacting through the naphtha cracker plant model.\n(Insert Simulink model screenshot)\nResults # The final control system performed well across all objectives:\nReactor temperature stayed within ±1°F of the 800°F setpoint throughout the simulation — a tight result for a process with continuous ambient temperature disturbances. Excess O₂ never dropped below 9%, satisfying the safety constraint. Fluctuations were larger than those seen on the temperature side, oscillating roughly between 10% and 15%, with a maximum of about 16.5%. Daily operating profit: $25,888.44 (Insert reactor temperature plot)\n(Insert excess O₂ plot)\nConclusion # We designed, implemented, and tested a multiloop control strategy for a naphtha cracker simulation using four controllers in total:\nPID controller on reactor temperature (feedback) Lead-lag controller on ambient temperature (feedforward) Variable ratio controller based on excess oxygen PID controller on air flow The system met every objective we set at the start of the project — stable temperature control, safe oxygen levels, and a positive daily profit. The main area with room to grow is the excess oxygen loop: the fluctuations there were noticeably larger than on the temperature side, which suggests that better fine-tuning or the addition of an override control strategy could improve air efficiency and push profit higher. Cascade control on the temperature loop is also worth exploring in future trials.\n","date":"29 November 2023","externalUrl":null,"permalink":"/projects/naphtha-cracker-control/","section":"Projects","summary":"Course Project for Process Dynamics and Control","title":"Naphtha Cracker Multiloop Control Strategy","type":"projects"},{"content":" Overview # The water gas shift (WGS) reaction converts CO and steam into H₂ and CO₂ over a packed bed catalyst. One of its main industrial uses is adjusting the CO:H₂ ratio of syngas streams to suit downstream synthesis — Fischer-Tropsch synthesis, for example, requires a 1:3 CO:H₂ ratio. This project models a combined WGS and CO₂ capture reactor and determines how long it should run before the buffer tank reaches that target ratio, at which point the system switches to CO₂ desorption.\nReactor Design # The reactor is a fixed bed with two packed sections in series. The first section carries a low-temperature WGS catalyst operating at 207°C, selected for its low cost, good stability, and compatibility with the target operating conditions. The second section is packed with a solid sorbent that captures CO₂ via physical adsorption at elevated temperatures, removing it from the product stream as it forms.\nThis arrangement lets both reactions happen simultaneously in a single vessel: the WGS reaction produces H₂ and CO₂, and the sorbent immediately captures the CO₂ downstream, keeping the product stream clean and shifting the reaction equilibrium further toward completion.\n(Insert reactor schematic)\nModeling and Results # The system was modeled in MATLAB by solving the coupled differential equations governing each species\u0026rsquo; concentration over time. Outlet concentrations were integrated to track the cumulative moles of CO and H₂ accumulating in the buffer tank, and the CO:H₂ ratio was monitored until it hit the 1:3 target.\n(Insert concentration vs. time plot)\n(Insert CO:H₂ ratio vs. time plot)\nThe target ratio is reached at approximately 91 seconds, giving a clear operational switching point between the adsorption and desorption phases.\nDiscussion # Integrating CO₂ capture directly into the WGS reactor is useful beyond just hitting a syngas composition target. Removing CO₂ in-situ shifts the reaction equilibrium, improves H₂ yield, and produces a captured CO₂ stream suitable for sequestration or further conversion — all without a separate downstream separation unit. The design is a small-scale illustration of how reaction and separation can be coupled to improve both efficiency and environmental outcome in fuel synthesis processes.\n","date":"3 May 2024","externalUrl":null,"permalink":"/projects/wgs-capture-reactor/","section":"Projects","summary":"Course Project for Kinetics and Reaction Engineering","title":"Combined Water Gas Shift Reactor with CO₂ Capture","type":"projects"},{"content":"","date":"23 April 2025","externalUrl":null,"permalink":"/projects/","section":"Projects","summary":"","title":"Projects","type":"projects"},{"content":"","date":"23 April 2025","externalUrl":null,"permalink":"/","section":"Simon Socha Gausachs","summary":"","title":"Simon Socha Gausachs","type":"page"},{"content":"All images used on this site are sourced from publicly available repositories and are used in accordance with their respective licenses. Attribution is provided below where required.\nThumbnail Page Usage Title Author/photographer name License type Link to source Homepage Card Background Forchem tall oil refinery, Rauma, Finland kallerna CC BY-SA 4.0 Link to source Sustainable Supply Chain Biodiesel Production from Waste Cooking Oil Cover Image Oil in the pan ImipolexG CC BY-NC SA 2.0 Link to source Startup of an Anaerobic Digester Cluster at a Wastewater Treatment Facility Cover Image Domes of the anaerobic digester plant at Egmere Evelyn Simak CC BY-SA 2.0 Link to source Combined Water Gas Shift Reactor with CO₂ Capture Cover Image PBRE-WR Preflight Imagery NASA Public Domain Link to source Naphtha Cracker Multiloop Control Strategy Cover Image 台塑六輕工業區 FPG\u0026rsquo;s naphtha cracker Yu-Chan Chen CC0 1.0 Link to source Azeotropic Distillation of Methanol and Toluene Cover Image Distillation Columns at Saltend Andy Beecroft CC BY-SA 2.0 Link to source ","externalUrl":null,"permalink":"/credit/","section":"Simon Socha Gausachs","summary":"All images used on this site are sourced from publicly available repositories and are used in accordance with their respective licenses. Attribution is provided below where required.\nThumbnail Page Usage Title Author/photographer name License type Link to source Homepage Card Background Forchem tall oil refinery, Rauma, Finland kallerna CC BY-SA 4.0 Link to source Sustainable Supply Chain Biodiesel Production from Waste Cooking Oil Cover Image Oil in the pan ImipolexG CC BY-NC SA 2.0 Link to source Startup of an Anaerobic Digester Cluster at a Wastewater Treatment Facility Cover Image Domes of the anaerobic digester plant at Egmere Evelyn Simak CC BY-SA 2.0 Link to source Combined Water Gas Shift Reactor with CO₂ Capture Cover Image PBRE-WR Preflight Imagery NASA Public Domain Link to source Naphtha Cracker Multiloop Control Strategy Cover Image 台塑六輕工業區 FPG’s naphtha cracker Yu-Chan Chen CC0 1.0 Link to source Azeotropic Distillation of Methanol and Toluene Cover Image Distillation Columns at Saltend Andy Beecroft CC BY-SA 2.0 Link to source ","title":"","type":"page"},{"content":" Simon Socha Gausachs, EIT # Odessa, FL | (813) 682-8990 | simon1@usf.edu\nEducation # B.S. Chemical Engineering — University of South Florida, Tampa, FL August 2021 – December 2025\nProfessional Experience # Process Development Researcher — University of South Florida, Tampa, FL June – December 2025\nDeveloped and optimized Aspen Plus simulation models of different configurations of operating units for supercritical transesterification of waste cooking oil. Performed a techno-economic analysis of equipment sizing and selection for a batch reactor for enzymatic transesterification of waste cooking oil. Regressed ternary glycerol-ester-ethanol equilibrium data to refine phase behavior modeling of decanter separations in Aspen Plus. Supported lab activities involving HPLC and enzymatic reactions. Process Engineer Intern — PegasusTSI, Tampa, FL July – August 2023\nDeveloped and refined a complex hydraulic model for a 15,000 GPM multi-loop cooling water system using AFT Fathom. Prepared standard calculation templates for sizing rupture disks and pressure relief valves per the API-520 Standard. Generated PFD stream tables for a Rare Earth facility across multiple scenarios by interpolating third-party heat and material balance data in CHEMCAD and Excel macros. Developed Visual Basic programs to automate information transfer and spreadsheet formatting for PFD stream table development. Process-Mechanical Engineer Intern — Stantec, Tampa, FL July – August 2022\nDeveloped an Excel/VBA tool to calculate daily feed rates for four anaerobic digesters during startup, based on lab results using mass balance calculations and recommended operating ranges. (Project Article) Validated and updated contents of valve, pipe, and equipment schedules by cross-referencing P\u0026amp;IDs and mechanical drawings. Collaborated on an interdisciplinary review for a 4,800 cfs stormwater pump station and reservoir project. Troubleshot a pump station hydraulic model in Pipe-Flo to determine deviations against field operating data. Developed a MATLAB script for dynamic simulation of a digester overflow event to size required sludge containment. Capstone Design Project # Project Lead — \u0026ldquo;A Sustainable Supply Chain Biodiesel Production System from Waste Cooking Oil\u0026rdquo; (Project Article)\nFocused on the reuse of waste cooking oil in the Tampa Bay area using enzymatic and supercritical transesterification in a two-stage process. Researched process designs, conceptualized base configuration, developed mass and energy balances, and integrated the design in an Aspen Plus simulation including a heat exchanger network, optimized separation train, reaction system, and dynamic control scheme. Coordinated group efforts to develop an economic analysis, batch reactor modelling, PFDs, P\u0026amp;IDs, HAZOP/LOPA analysis, and the final report. Other Key Projects # Developed a multiloop control strategy for a naphtha cracker in Simulink to maximize profit by regulating excess oxygen and furnace temperature via fuel and air feed adjustments. (Project Article) Developed an extractive distillation process model in Aspen Plus for the separation of methanol and toluene, optimizing cost by adjusting stage count and reflux ratio using sensitivity analysis. (Project Article) Sized a combined water-gas shift and CO₂ adsorption reactor in MATLAB to adjust the CO:H₂ ratio of a syngas gasifier feed in a sustainable and cost-efficient manner. (Project Article) Skills # Process Engineering \u0026amp; Design\nProcess simulation, mass and energy balances, process optimization, PFD/P\u0026amp;ID development, equipment sizing, process scale-up, techno-economic analysis, lifecycle cost and feasibility evaluation.\nSimulation \u0026amp; Modeling Software\nAspen Plus, CHEMCAD, AFT Fathom, Pipe-FLO\nProgramming \u0026amp; Data Analysis\nPython, MATLAB \u0026amp; Simulink, Excel VBA/Macros\nEngineering Tools\nMicrosoft Office, Power BI, Visio, SolidWorks (CSWA Certified)\nProcess Improvement \u0026amp; Operations\nProcess troubleshooting, root cause analysis, statistical process control, process validation\nSafety \u0026amp; Compliance\nPressure relief system design (API 520), HAZOP and LOPA analysis, process safety management (PSM)\nCertifications # Engineer in Training (EIT) — Chemical Engineering, certified January 2026 AIChE SAChE Safety Certifications (February 2023 – November 2024) Languages # English (fluent), Spanish (fluent)\nU.S. citizen — authorized to work for any employer, no sponsorship required.\n","externalUrl":null,"permalink":"/resume/","section":"Simon Socha Gausachs","summary":" Simon Socha Gausachs, EIT # Odessa, FL | (813) 682-8990 | simon1@usf.edu\nEducation # B.S. Chemical Engineering — University of South Florida, Tampa, FL August 2021 – December 2025\nProfessional Experience # Process Development Researcher — University of South Florida, Tampa, FL June – December 2025\n","title":"","type":"page"},{"content":" Hi, I\u0026rsquo;m a Chemical Engineering graduate from the University of South Florida, originally from Concepción, Chile. I moved to Florida when I was nearly 6 years old and have lived here since.\nEngineering runs in my family. My dad is a process engineer and my grandfather was a chief operator at a petroleum refinery in Chile, so I\u0026rsquo;ve been exposed to what process engineering is really like from a young age and have been personally invested in it for a long time. Throughout my education, internships, and research experience I\u0026rsquo;ve built up a well-rounded background incorporating process modeling, design, and analysis including Aspen Plus and CHEMCAD simulations, hydraulic and control systems, P\u0026amp;IDs, hazard analyses, and leading a multidisciplinary capstone project from concept to economic evaluation.\nAn elective course on synthetic fuel production sparked an interest in how different process technologies can integrate to improve efficiency and sustainability. Renewable energy has a fundamental tension at its core: most of its value in addressing environmental, climate, and economic sustainability isn\u0026rsquo;t quantifiable in a conventional sense, which conflicts with a world where short-term profit drives investment. Process engineers are tasked with bridging that gap by providing pragmatic solutions that simultaneously deliver quantifiable and non-quantifiable value. That challenge is what drew me to choose sustainable biodiesel production from waste cooking oil as my senior design project topic and eventually led me to continue contributing to related research after the project concluded.\nEngineering isn\u0026rsquo;t something I leave at work. On the software side, that includes developing a Discord bot to manage a custom ranking system among friends and building a declarative Linux configuration using NixOS. On the hardware side, I\u0026rsquo;ve worked on a mini-vehicle controlled by voice commands interpreted using local AI models and helped my dad manage the water treatment system for our house. I\u0026rsquo;m also a strong advocate for free and open source software, right to repair, and data privacy as well as the idea that buying something should mean owning it.\nOutside of technical problems, I enjoy traveling, the outdoors, and riding my bike. Whenever I\u0026rsquo;m not working on something, you might catch me trying to beat my Minesweeper time or doing chess puzzles.\n","externalUrl":null,"permalink":"/aboutme/","section":"Simon Socha Gausachs","summary":" Hi, I’m a Chemical Engineering graduate from the University of South Florida, originally from Concepción, Chile. I moved to Florida when I was nearly 6 years old and have lived here since.\nEngineering runs in my family. My dad is a process engineer and my grandfather was a chief operator at a petroleum refinery in Chile, so I’ve been exposed to what process engineering is really like from a young age and have been personally invested in it for a long time. Throughout my education, internships, and research experience I’ve built up a well-rounded background incorporating process modeling, design, and analysis including Aspen Plus and CHEMCAD simulations, hydraulic and control systems, P\u0026IDs, hazard analyses, and leading a multidisciplinary capstone project from concept to economic evaluation.\n","title":"About Me","type":"page"},{"content":"","externalUrl":null,"permalink":"/authors/","section":"Authors","summary":"","title":"Authors","type":"authors"},{"content":"","externalUrl":null,"permalink":"/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":"","externalUrl":null,"permalink":"/series/","section":"Series","summary":"","title":"Series","type":"series"},{"content":"","externalUrl":null,"permalink":"/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"}]