The aim of my MASc thesis was to develop a new energy model that predicts the energy infrastructure required to maintain the oil production in the Oil Sands operations at minimum cost. The key novelty in my research is that the model searches simultaneously for the most suitable set of oil production schemes and the corresponding energy infrastructures that satisfy the total production demands under environmental constraints, i.e., CO2 emissions targets. The proposed modeling tool was validated using historical data and used to study the 2020 Oil Sands operations under three different production scenarios. Also, the 2020 case study was used to show the effect of CO2 capture constraints on the oil production schemes and the energy producers. The results show that the proposed model is a practical tool to determine the production costs for the Oil Sands operations, evaluate future production schemes and energy demands scenarios, and identify the key parameters that affect the Oil Sands operations. The respective publications of this project are available in my Google Scholar Profile.
The aim of my PhD study was to develop, test and validate a dynamic model of the CO2 capture and purification unit (CO2CPU) for oxy-coal-fired power plants. The main challenge overcome in this research was to develop a dynamic model of a multi-stream heat exchanger that involves multiple process streams and encounters both condensing and boiling two phase flows. The proposed multi-stream heat exchanger model was based on a shell and tube configuration that considers only axial changes in flow, i.e., a 1D model. Likewise, the two phase region in this unit was modelled using a homogenous model, which is a simplified discretized two-phase flow model that reduces the computational effort and complexity of the multi-stream heat exchanger process model. To my knowledge, the model developed in this study represented the first mechanistic process model that describes the transient behaviour of a CO2CPU for oxy-fired power plant. Two design configurations of the CO2CPU were considered in this study, i.e. the Air Products’ CO2CPU and the CanmetENERGY’s proprietary CO2CPU (CanCO2). Both plants were designed based on a two-stage flash separation process. Reasonable agreement between the developed models and the design data were obtained for both CO2CPU configurations. Several dynamic tests were performed to gain insight into the transient behaviour of the CO2CPU.
I obtained my Bachelor of Science in Chemical Engineering from the Benemerita Universidad Autonoma of Puebla (BUAP), Mexico. I am currently enrolled in the Master of Science in Chemical Engineering program at the Iberoamericana University, Mexico. In fall 2016, I had the opportunity to join Professor Luis Ricardez-Sandoval’s research team as a visiting MASc student scholar.
My research project is on Robust NonLinear Model Predictive Control of a Water-Treatment Aereation Sequencing Batch Reactor (SBR). The removal of pollutants from wastewater is a public health problem that demands technically efficient and cost competitive strategies. The use of sequencing batch reactors (SBR) is one of the main innovative alternatives that has been used for the treatment of water effluents. The problem under consideration consists in computing closed-loop optimal control policies of the aeration profiles under uncertain conditions such that the target values determining water quality are reached in minimum operating time. We propose a robust nonlinear model predictive control scheme to deal with uncertainty in the process model parameters. A sensitivity analysis was carried out to identify the model parameters featuring the strongest influence on model response. The uncertainty was modeled as bounded uncertainty on selected model parameters and a multi-scenario optimization approach was then used for its discretization.
My MASc research studies focused on the development of an algorithm to schedule operations in an actual large scale analytical services plant using models based on multi-commodity flow (MCF) and integer linear programming (IP) techniques. The proposed scheduling algorithm aims to minimize the total turnaround time of the operations subject to capacity, resource and flow constraints. The working principles of the optimization-based algorithm are illustrated with a real large-scale plant. The algorithm’s results were compared against historical data and results obtained by simulating the current policy implemented in the real plant, i.e., first-come first-served. As part of these research studies, I also developed a new methodology that can address three aspects of the economy of the multiproduct processes together; i.e. simultaneous scheduling, design and control. The proposed methodology takes into account the influence of disturbances in the system by the identification of the critical frequency from the disturbances, which is used to quantify the worst-case variability in the controlled variables via frequency response analysis. The uncertainty in the demands of products has also been addressed by creating critical demand scenarios with different probabilities of occurrence, while the nominal stability of the system has been ensured. Case studies involving large-scale nonlinear systems have been developed as applications of the methodology.
I am a PhD student co-supervised by Prof. Luis and Prof. Eric at the Chemical Engineering Department of the University of Waterloo. My research mainly focus on solid oxide electrolysis cell (SOEC) for the direct conversion of CO2 to carbon monoxide (CO). Co-electrolysis of steam and carbon dioxide using high temperature electrolysis cells to fabricate syngas has been attracting widespread interest for carbon capture and utilization because it is a novel method of reutilization of the captured carbon dioxide (CO2). However, cathode materials currently used for HT-SOEC have not met the requirements of satisfactory electrochemical performance and high electrolysis efficiency. CO2 reduction reaction mechanism on SOEC cathode materials will be studied in my research to explore the structural, electronic and energetic properties of cathode catalysts using Density Functional Theory (DFT) analysis. These properties are key to improve the design and operability of catalytic processes. Molecular simulations will also provide insight and guide my experiments for cathode catalyst preparation and fabrication of highly efficient SOEC.
I received my BASc in Chemical Engineering from the University of Waterloo
in 2015. The aim in my research is to study scheduling optimization methodologies and applicationofvarious Mixed Integer Linear Programming (MILP) formulations for industrial sized problems. We are currently working with an industrial partner in the analytical services sector. In particular, I have explored, and will continue to look for ways to effectively handle various type of uncertainties that may become realized, for example, uncertainties inprocessing capacities, that can be explicitly considered in the optimal schedule. There are several methods that can be implemented to handle uncertainties for optimization, such as stochastic optimization, and robust optimization; however, implementation of those methods can significantly increase computational time, and/or result in overly conservative solutions. In our group, we are also actively looking for ways to solve deterministic versions of these scheduling problems through implementation of efficient discretization of the multiple time scales of a non-uniform discrete time formulation, and smart design of the objective function. Being able to solve deterministic scheduling models faster will allow us to add more features to the model such as consideration of uncertainties, and simultaneous scheduling of both machine and human resources.
Catalytic flow reactors play an important role in chemical processes, and as a result there has been substantial interest toward reactor development and optimization. The behaviour of catalytic reactors depends on the macroscopic reactor design as well as the microscopic events taking place on the catalyst surface. As a result, both scales must be accounted for while modeling and designing new and optimal reactors. However, this is significantly impeded by uncertainties present in the reactor’s kinetic mechanisms and system parameters. In catalytic flow reactors, uneven catalyst fouling produces spatially-varying uncertainties in the reaction rates along the reactor length. My research focuses on the design of robust optimization and control studies for the catalytic flow reactor to identify the values of key design and operation parameters that maximize the reactor performance and minimize the effects of uncertainty. This is accomplished through the use of reduced-order models such as Power Series Expansions (PSEs), Polynomial Chaos Expansions (PCEs), and data-driven models (DDMs) to efficiently propagate spatially-varying uncertainty through the catalytic reactor model. The objective is to address the effects of uncertainty while designing control and robust optimization processes for transient and steady-state multiscale catalytic flow reactors.
I studied in the Nanotechnology Engineering honours program at the University of Waterloo and graduated with my BASc degree (with distinction) in 2013. I then pursued a MASc degree in Chemical Engineering at the University of Waterloo. In my research, I used an existing pairwise potential energy formulation but developed new strength and range functions for the potential in order to capture anisotropic and asymmetric interactions between different particle pairs (rod-rod and rod-sphere). I used the potential to perform coarse-grained molecular Monte Carlo simulations of liquid crystal phases and liquid crystal-nanoparticle mixtures. In 2013, I was awarded a Nanofellowship from the Waterloo Institute for Nanotechnology (WIN). I presented my work at the 2015 Compute Ontario Research Day (CORD) and received the best oral presentation award. My work has also been presented at the 2015 Gordon Research Conference (GRC). I joined Professor Luis' research group as a PhD student in the fall of 2015 to develop new and efficient methods for optimization and control of multiscale process systems under uncertainty using efficient uncertainty propagation methods such as Power Series Expansion (PSE) or Polynomial Chaos Expansion (PCE).
I graduated from Beijing Institute of Technology (BIT) in 2016 and continued to study at the University of Waterloo a MASc program. Currently, I’m interested in modelling oxygen carrier particles used in the Chemical Looping Combustion (CLC) process.
CLC decomposes fuel combustion reaction into several sub-reactions. Firstly, particles consisting of metal and support materials react with air in the oxidation reactor, converting metal into corresponding metal oxide. Oxidized particles are then transported into a reduction reactor where metal oxides react with fuel gas such as syngas and natural gas, releasing energy while generating a sequestration-ready CO2 stream. Particles containing reduced metal are recycled to the oxidation reactor to start the next cycle. In this process, the metal/metal oxide dual carries the oxygen from the air to the reduction reactor so direct contact between air and fuel is avoided.
The practicability and productivity of CLC strongly depends on the effective performance of oxygen carrier particles. The desired oxygen carrier particles should keep high reaction and conversion rates during long-term operation with hundreds of cycles. Therefore, it is critical to understand the causes of oxygen carrier deactivation. One speculation is that the particle structure changes due to sinter and/or attrition thus reducing the porosity. This may lead to a significant decrease of reaction surface. The goal in my research is to develop particle models that can explicitly account for interactions that occur in the particle and that are not explicitly considered in the current models, e.g. the effect of porosity. Through my work, new insight into the design and selection of desired oxygen carrier particles will be provided.
The development of clean energy production in order to mitigate the CO2 emissions is the main focus of actual research studies. Climate change, as a consequence of the excess of CO2 emissions from anthropological activities, is the most significant reason to develop new technologies in the energy sector. While renewable energies may be an attractive alternative resource of energy, carbon based power plants still remain as the main source of energy, and it will continue to do so for the coming decades. Post-combustion CO2 capture will play an important role on reducing this gas emissions. Also, it is a mature technology that can be easily integrated to existing power plants. However, this process still needs to be optimized in order to improve their efficient and minimize their impact on the operability and economics on the existing coal-based power plants.
The aim of my research is to analyze the controllability and dynamic operation of a post-combustion CO2 capture plant model using as main solvent Piperazine (PZ). The key in my research is to develop a Model Predictive Control (MPC) framework for this process. The proposed MPC scheme and this new promising solvent will be compared with previous studies to gain insight on the benefits and limitations on using MPC controllers and PZ solvent for post-combustion CO2 capture plants.
My research mainly focus on multi-scale simulation of the catalytic reaction, e.g. at microscopic, molecular or even electronic scales. The modelling techniques I am using are mainly Density Functional Theory (DFT), Kinetic Monte Carlo (KMC) and Finite Element Analysis (FEA). I have extensive research experience in these catalytic processes:, carbon nanotube (CNT)/graphene growth, syngas/hydrogen production, oxygen reduction reaction (ORR) in electrochemical reaction. The respective publications of this project is available in here.
I joined Luis’s group for my PhD in spring 2017. Currently, I am working on Optimization Scheduling for large-scale industrial production. Various optimization studies are being conducted on different production plants with a motive to develop a suitable model using which the plant functioning can be efficiently scheduled in such a way to achieve maximum profit in minimum make span with effective utilization of the resources. We are working with an industrial partner from analytical services sector. We have so far developed a STN (State-Task Network) based non-uniform time discrete scheduling model for large-scale plants and are currently trying to incorporate uncertainty into the large scale model
Canada Research Chair (CRC) Tier II in Multiscale Modelling and Process Systems
Associate Professor of Department of Chemical Engineering
Member, American Institute of Chemical Engineers (AIChE)
Member, Professional Engineers Ontario (PEO)
Member, Canadian Society for Chemical Engineering (CSChE)
Phone: +(519)-888-4567, x38667
Location: E6 3014
Ph.D., University of Waterloo, 2008.
MASc, Technological Institute of Celaya, 2000
BASc, Technological Institute of Orizaba, 1997
I graduated with a BASc in Chemical Engineering from the University of Waterloo in 2016. During my undergraduate studies, the projects that I found most interesting where those involving computational software like MATLAB and Aspen Plus. In my fourth year I completed an Undergraduate Research Assistantship with Dr. Luis focused on uncertainty propagation in models using Polynomial Chaos Expansion (PCE). I am now a MASc student in his research group and was awarded an Ontario Graduate Scholarship for my high academic standing.
The main project of my thesis is the application of Adjustable Robust Optimization (ARO) to design robust Model Predictive Control (MPC) systems. MPC is a powerful control tool as it has the ability to drive a process to a desired set point in an optimal fashion while remaining feasible with respect to any constraints that have been imposed. The main disadvantage is that the optimization problem which makes up the MPC controller must be solved at every time step in the process. ARO has the potential to make MPC amenable to larger processes by significantly reducing the computational time required to calculate the following control action. Like other explicit MPC schemes, rather than solving the optimization problem at every time step, the problem is solved offline. The result is a set of linear control laws which are a function of the state of the system. To determine the following control action, the controller simply evaluates the control laws based on the measured state rather than solving an optimization problem.
I graduated with a BASc. in Chemical Engineering from Universidad La Salle, in Mexico City in 2011. Later I worked for 4 and a half years in an Engineering Procurement and Construction (EPC) company as a process engineer. After gaining some professional experience, I realized that an excellent skill complement would be pursuing a Masters degree. Currently, I am a MASc. student at the University of Waterloo and I was awarded with the CONACYT-SENER Scholarship to fund my research.
Climate change has been and is going to continue a major challenge for humanity. Concentration of greenhouse gases (GHG) in the atmosphere is directly linked to the average global temperature on Earth. The most abundant GHG is CO2, product of burning fossil fuels. The use of fossil fuels is likely to continue in the near future, thus is mandatory to reduce CO2 emissions to the atmosphere, specially in power generation which is responsible for approximately 30% of the CO2 emissions.
One of the most exciting and economically attractive technologies is Chemical looping combustion (CLC). The scope of my research is to set a dynamic model that simulates the CLC packed bed reactor behaviour and later analyze the controllability of such process. This will provide more knowledge about CLC and the feasibility of his large-scale implementation.
Solar energy is one of the most promising alternatives since it represents an inexhaustible and free source of energy. The aim of my MASc thesis is related to dynamic modelling of a solar thermal collector on the GAMS environment. The key idea is to reduce the use of fossils fuels by taking advantage of solar energy through a solar thermal collector. The main objective in my research is to find the appropriate operating conditions for this process that can maximize power output in the presence of changes in the environment. The dynamic model applied to determine the solar thermal collector output temperature is represented by a set of differential equations that were discretized using orthogonal collocation on finite elements. The operation of the solar thermal collector involves integer decisions since multiple units can be turned on/off depending on the external factors affecting the plant and energy requirements. Hence the proposed optimization model was formulated as a Mixed Integer Nonlinear Programming (MINLP) problem that was implemented on the GAMS environment. Different scenarios have been analyzed taking into account changes in the weather.
I received the BASc degree in Chemical Engineering in 2010, from Sahand University of Technology. I graduated in 2012 from my MASc program in Chemical Engineering (Modeling, Process Simulation and Control) from Sharif University of Technology . During my MASc, I developed research on Modelling of Reactive Crystallization Processes, which concerns with the modeling of precipitation processes aimed at predicting product particle characteristics with special emphasis on particle size distribution. A mixing-precipitation model linked to the population balance, which takes into account the agglomeration mechanism, was used for a single-feed semi-batch precipitation process to describe the mixing effects on the crystal size distribution (CSD) and mean size at the end of the batch. I have joined Dr. Luis’s group in the fall of 2015 to start with my PhD studies; my research will focus on the development of new computationally efficient methods that perform the optimal process and control design of large-scale chemical processes in the presence of external perturbations and uncertainty in the systems parameters.
Solid phase microextraction (SPME) is a rapid growing sample preparation technique used for analytical applications. We have developed a mechanistic mathematical model for the processes occurring in SPME extraction of analyte from a sample. The parameters that contribute to the uptake kinetics of the SPME has been computationally simulated with Comsol MultiphysicsTM. We are currently developing mathematical models which will provide a clearer understanding of the SPME processes and optimum experimental parameters without the need to perform a large number of experiments in the laboratory. This research project is in collaboration with Professor Janus Pawliszyn of Department of Chemistry, University of Waterloo. The respective publications of this project is available in here.
A dynamic MEA absorption process model was developed in my PhD to study the operability of this process in a dynamic fashion and to develop a control strategy to maintain the operation of the MEA scrubbing CO2 capture process in the presence of the external perturbations, which may arise from the transient operation of the power plants. The novelty in this work is that a mechanistic model based on the conservation laws of mass and energy have been developed for the complete MEA absorption process. The mechanistic dynamic model was applied to develop a basic feedback control strategy. The implementation of a control strategy was tested by changing the operating conditions for the flue gas flow rate. The model developed in my PhD research work have used as a basis to develop other studies related to the operability, controllability and dynamic flexibility of this process. The respective publications of this project is available in here.
I developed in my MASc studies a new efficient approach for the optimal design under uncertainty. The key idea is to approximate the process constraint functions and outputs using Power Series Expansions (PSE)-based functions. A ranking-based approach was adopted where the user can assign priorities or probabilities of satisfaction for the different process constraints and process outputs considered in the analysis. The methodology was tested on a reactor-heat exchanger system, the Tennessee Eastman plant, which is an industrial benchmark process, and a post-combustion CO2 capture plant, which is a large-scale chemical plant that has recently gained attention and significance due to its potential to mitigate CO2 emissions from fossil-fired power plants. Furthermore, a stochastic-based simultaneous design and control methodology for the optimal design of chemical processes under uncertainty that incorporates a Model Predictive Control (MPC) scheme was also developed during my MASc studies. The key idea is to determine the time-dependent variability of the system that will be accounted for in the process design using a stochastic-based worst-case variability index. The MPC-based simultaneous design and control approach provided more economical designs when compared to a decentralized multi-loop PI control strategy, thus showing that this method is a practical approach to address the integration of design and control while using advanced model-based control strategies such as MPC. The respective publications of this project is available in here.
The microelectronic market imposes tight requirements upon thin film properties, such as surface roughness and film thickness. Thin film deposition is a batch process where the microscopic events determine the configuration of the thin film surface. However, manipulating the process at the macroscopic level is essential to the product quality. There are three major obstacles for developing efficient control methodologies for thin film deposition in the semiconductor industry: i) development of fundamental mathematical models describing the multiscale nature of this process, ii) lack of practical in-situ sensors that provide real-time measurements for online control, and iii) uncertainties in the microscale mechanisms and parameters of the system. The main goal of my research is to address these challenges for robust control and optimization purposes in the multiscale thin film deposition process. The respective publications of this project is available in here.
Today's world is extremely competitive; businesses have to be aggressive and develop or implement novel technologies that would allow them to reduce costs while meeting the process targets. One of the most effective ways to reduce operational costs is through the implementation of optimal operating policies (scheduling). One of the main questions that arise when performing optimal scheduling is regarding the time representation in the scheduling formulation. We can assume time to be either constituted of discrete event points or to be a continuous flow (To best of our knowledge this is a theoretical physics question as well; to know whether the fabric of time is continuous or discrete). The aim of my research is to determine which one of these two representations would result in a better scheduling model. We are also looking for other possibilities, specifically whether it is possible to discretize time for some processes while representing time for some other processes in a continuous fashion. Some of our criteria for this comparison are quality of the optimal solution, flexibility of the model, complexity and computational time of the model and easiness of implementation for the production facility or the operational plant. We are working towards these aims in a project that involves an industrial partner in the analytical services sector, which would allow us to implement our scheduling approaches in an actual plant.
I completed the bachelor degree in Chemical Engineering in 2007, from King Mongkut University of Technology North Bangkok. Also, I have four year experience in upstream oil and gas industry prior to pursuing the graduate study in University of Waterloo. In my MASc research work, a mechanistic dynamic model of a pilot plant of a post-combustion CO2 capture plant using the monoethanolamine (MEA) absorption processes was developed. The proposed dynamic model was then used to design promising decentralized control schemes for this pilot plant. The performance evaluation of those control structures was assessed under multiple scenarios, e.g. changes in flue gas composition, set point tracking, valve stiction. Furthermore, a step-by-step method to scale-up an MEA absorption plant for CO2 capture from a 750 MW supercritical coal-fired power plants was developed in my research. The mechanistic model of an industrial-scale CO2 capture plant including a proposed control structure was evaluated using different scenarios. The plant has been shown to handle different disturbances and offers prompt responses. In addition, poor wetting in the strippers can be avoided by the implementation of suitable operating policies (process scheduling) in the plant.
I graduated from my BASc in 2017 from the University of Guadalajara. After receiving a grant from CONACyT, I was able to join Luis' research group and started my MASc in the Fall term of 2017.
Currently, I am researching an approach to integrate scheduling and optimal process operation of batch plants under uncertainty. Common scheduling methods are limited, in a way such that they imply the successful completion of a specific processing recipe, this is not always true. Process uncertainty, is a critical factor that in conjuction with disturbances and fluctuating demand, will impact and effectively null the validity of a schedule. As such, it is important to develop a computationally efficient method, able to integrate the aforementioned factors to generate an optimal schedule, maximizing process revenue, while meeting manufacturing and dynamic constraints under the influence of uncertainty.
Preliminary results show that a multi scenario approach is able to efficiently link process dynamics and scheduling while not being a computationally demanding method.
My doctoral research studies focus on improving CO2 capture process by adopting Chemical Looping Combustion (CLC). The process of CLC avoids direct contact between the fuel and the air. This process is based on the transfer of oxygen from air to fuel by means of a metal based oxygen carrier with inherent sequestration of CO2, which is one of the largest contributors to the greenhouse gas effect. The key challenge in CLC system is related to the performance of the oxygen-carrier material. The aim in my research consist in elucidating the reaction mechanisms of CLC at multiple scales that will provide new fundamental insight on ways that can enhance the oxygen transport capacity, improve the reaction activity and prolong the lifetime of oxygen carriers. Density Functional Theory (DFT) combined with kinetic Monte Carlo (kMC) method will be mostly used in my research to perform the first-principles studies and multi-scale simulations.
Operations or process scheduling is a problem whose relevance spans many different industrial and engineering fields such as mining, chemical production and manufacturing plants. The goal of process scheduling is to find an optimal schedule that can efficiently carry out operations at minimizing costs, in the shortest possible time (minimize turnaround time), or to maximize revenue. Following schedules that are tailored to meet the demands can greatly increase the overall efficiency of the operations or reduce overhead. There has been a great amount of research done in the field of process scheduling, both for theoretical purposes and the practical reasons mentioned above. However, the field still remains rich as more complex models made to more closely resemble real world conditions are considered. My research is focused around devising new methods and strategies for solving a real world scheduling problem at an analytical services facility. In practice with large facilities and moderately sized time horizons, finding optimal schedules is computationally expensive. The goal of my work is to find “good” quality solutions while keeping computational costs reasonable so that these strategies may be employed in practice. Since it is known that solving general integer programs (IPs) is NP-hard, I am not aiming to find algorithms that are necessarily theoretically efficient for solving the problem, I am interested in developing heuristics that perform well on the problem in practice.
CO2 emissions are regarded as one of the key factors that affect global warming. Significant efforts have been made to develop efficient and economically attractive technologies to reduce to CO2 emissions. Post-combustion MEA-based CO2 capture is regarded as a realistic and viable technology that can be implemented to capture CO2 from existing coal-fired power plants. My research is focused on the development of dynamic flexibility studies for post-combustion MEA-based CO2 capture plants. The flexibility analyses considered in my research have made use of Model Predictive Controllers (MPC), which is at the state-of-the-art in process control. Although MPC has been widely applied to processes from different sectors, its implementation in CO2 capture plants is relatively new; hence the motivation to demonstrate its applicability and significant benefits for this type of processes. In addition, I also pursue in my research the development of simultaneous scheduling and control frameworks for the design of optimal operating and control policies for post-combustion CO2 capture plants. As shown in the figure below, our current scheduling and control framework have shown to improve the dynamic performance of a CO2 plant when compared to that obtained from a traditional sequential scheduling and control approach.