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Current Research Topics
Preliminary Conversion of Algae-Derived Carbohydrates
UND Faculty Partner: Wayne Seames,ChE
UoL/ERI Research Partner: Andrew Ross
Abstract: Under certain conditions, the decomposition of glucose can yield lactic acid and its derivatives. Biomass-derived carbohydrate-based industrial chemicals have the potential to be renewable replacements to chemicals derived from fossil fuels. Previous research has indicated that the use of a tin doped or acid activated zeolite catalyst drives the glucose deactivation reaction in favor of lactic and levulinic acid, which are useful chemicals and greatly decreases the production of unwanted by-products. Algae oil has been studied heavily for its use as a triglycercide oil feedstock for the production of biofuels. However after the extraction of the oil, a biomass residual remains composed largely of proteins and carbohydrates. The protein is sold as an animal feedstock, however the carbohydrates have the potential to be turned into high valued chemicals through the decomposition utilizing the optimum zeolite catalyst.
Student Scope of Work: You will demonstrate the feasibility of decomposing algae-based carbohydrates into lactic and levulinic acids using previously developed experimental and analytical methods. Model compounds will be used to model the feedstock and to conduct decomposition reactions. HPLC will be used to analyze the products. The reaction conditions will be varied to define the optimum reaction space. Preliminary work at UND will include a literature search of the correct carbohydrates as well as learning the experimental and analytical techniques required. Experiments will be conducted in Leeds using a batch autoclave reactor along with GC analytical methods.
Using a Dual-Shaft VAWT with a Dual-Rotor Generator
UND Advisor: Marcellin Zahui, ME
UoL Advisor: Andrew Shires
Vertical Axis Wind Turbines (VAWT) may be a low cost renewable energy source that can be used to bring much needed electricity or clean water to the developing world or in rural applications. A large segment of the world’s population relies on wood or charcoal as their principal sources of energy. Bringing electricity to those areas with conventional grid-based distribution will take too long and cost too much money. Therefore an off-grid solution as provided by the VAWT is the best chance to 1) quickly provide electricity to developing countries while 2) reducing adverse effect of fossil fuel based alternatives. VAWTs usually have two blades in lift or drag mode. One of the blades catches the wind to create a lift or drag to turn the shaft, bringing the opposing blade into the flow of the wind. This blade then repeats the process, causing the shaft to rotate further and completing a full rotation. This process continues all the time the wind blows and the turning of the shaft is used to drive a pump or a generator. These types of wind turbine are easy to maintain and have lower construction and transportation costs than conventions turbines. However, they are less efficient. They operate in lower and more turbulent wind and the blades are constantly spinning back into the wind causing drag. These disadvantages can be overcome with the use of two coaxial shafts with rotation in opposite directions due to blades design that allows the blades to capture wind flowing on each side of the vertical axis. The energy from both shafts can be added mechanically using gears or electrically using specially designed generators. The latter is proposed here for simulation and experimental verification. A generator is composed of a stator and a rotor. The proposed generator will have two rotors rotating in opposite directions thus increasing the apparent flux velocity and therefore generating more power.
Student Project Scope of Work: Build a small two-shaft VAWT and/or modify a DC generator into a two-rotor generator at UND and test the generator at Leeds
Ocean Wave Energy Converters and Their Impact on Power Systems
UND Advisor: Iraj Mamaghani, CE
UoL Advisor: Nikolaos Nikitas
Ocean wave energy is a non-polluting and renewable energy source with a large potential that may contribute to the worldwide increasing demand for power. Ocean wave energy is created by natural transfer of wind energy above the oceans, which itself is created by the effects of the sun’s solar energy. As the wind blows across the ocean’s surface, moving air particles transfer their energy to the water molecules that they touch. High variability rates over both short and long time scales characterize this concentrated form of solar energy. The energy associated with the surface ocean waves can be extracted by means of properly designed devices, according to various principles and following different concepts, and thus converted into electricity. Particular attention must be paid to the survivability of the systems during extreme loads in storms. As a consequence, the achievement of a satisfying balance between efficiency and reliability in any cost-effective wave energy tapping scheme leads to tough engineering challenges.
Student Scope of Work: The objective of this research is to introduce an enhanced ocean wave energy converter and analyze its impact on power systems. The potential of wave energy is very large. There are a lot of different methods and systems for converting this power into electrical power, such as oscillating water columns, hinged contour devices as the Pelamis, overtopping devices as the wave dragon and the Archimedes wave swing. The different types of wave energy conversion systems and main characteristics of these wave energy converters will be reviewed and one or more of these systems will be selected for testing.
Creep Response of Magnesium/Carbon Nanotube Nanocomposites: High-Strength Lightweight Structures
UND advisor: Meysam Haghshenas, ME
UoL Advisors: Robert Cochrane and Aidan Westwood
Metal matrix composites (MMCs) reinforced with nano-particles and in particular magnesium-based nanocomposites, provide a lightweight but strong structure that can save weight in vehicles and thus reduce energy usage. When magnesium alloys, the lightest engineering alloys but with limited strength, are mixed with reinforcement particles, i.e. carbon nanotubes (CNTs), the end product may effectively improve both the strength and ductility properties of the material.
Student Scope of Work: In this project, the student will study the creep response of a family of Mg-CNT nanocomposites at room and ambient temperature through an instrumented nano-indentation testing technique. The fabricated composites, with three different volume fractions of CNTs, will be provided to the students and the student will conduct initial testing at UND, such as nano-indentation creep at different strain rates and holding times. More complete testing will be conducted at UoL [advisors to add details on the full scope of the project] to assess the effect of these parameters on creep response of the materials. Depending on the progress and timing, tests can be done in one elevated temperature, i.e. 300 °C, as well.
Agent Based Modelling of Industrial Symbiosis
UND Advisor: Scott Johnson, IES
UoL Advisors: Frans Muller and Tim Cockrill
Industrial symbiosis is an important factor in optimizing the reuse of waste energy and materials, thus making manufacturing more sustainable. Typical approaches to modelling manufacturing systems are the (i) conventional engineering models simulating process plant in detail (e.g. aspen) and (ii) agent based models based on linear input-output models. System optimization of engineering models is typically against simple criteria (e.g. yield, cost) but are unsuitable for optimization of a network of such plants. Agent based models on the other hand can describe the effect of complex decision models on networks of factories, however these assume simple linear and instantaneous conversion of raw materials to product. We have developed a new code (in python) that combines a non-linear approach to processing with the complex decision models available in agent based modelling.
Student Project Scope of Work: In this project the student will develop the programming skills required to develop further the non-linear process aspects of Factory agents, and then build a number of case studies that demonstrate the impact of the process on the decisions taken by a network of agents.
Transient N2O Emissions from Algae Production
UND Advisor: Wayne Seames, ChE
UoL Advisor: Miller Camargo-Valero
Nitrous oxide (N2O) has been found in the gas emitted from a pilot-scale maturation pond used for polishing the effluent of a primary facultative pond treating domestic wastewater in the UK. Despite the lack of direct evidence, the foundation for a possible “assimilatory pathway” to N2O synthesis via nitrite and nitrous oxide (NO) can be found in a handful studies on phototrophic cells. Furthermore, “abnormal” correlations between microalgae activity and N2O emissions in aquatic ecosystems have been reported during several monitoring studies. Interestingly, However, there is simply not enough information known to allocate the origin of these emissions with certainty. The microalgal assimilatory pathway is challenging to establish because microalgae constantly regulate nutrient uptake depending on the environmental conditions they experience, especially with regard to light conditions. This regulation can cause nitrite to be synthesized and accumulate inside cells, leading to a situation where this potential substrate is readily available for N2O synthesis when photosynthesis is turned off at night. Detecting transient emissions requires high-temporal resolution monitoring but this is nearly never done in field studies, which in turn might explain why algal N2O production has largely gone undetected. There is currently a lack of understanding as to i) how and why N2O emissions occur, ii) if these emissions significantly impact the sustainability of microalgae cultivation, and iii) how these emissions may be reduced through targeted process operation.
Student Scope of Work: In this project the student will monitor steady-state and transient N2O emissions from representative microalgae bioprocesses. This research will provide a new foundation for understanding how N2O is released in aquatic environments and may challenge the current consensus that bacteria are responsible for most of the biological N2O emitted into the atmosphere.
On-line Bayesian-based Model-set Management Method for Steam Reforming Control with Various Feed Compositions
CPP Advisor: Jia Li, CME
Leeds Advisor: Professor Elaine Martin
Steam reforming is the most widespread process for the generation of hydrogen-rich synthesis gas from light carbohydrates. One of the operating and design challenges is that the feed materials are various from natural gas, shale gas, to liquid gas, or naphtha. The optimal operating conditions identified under one type of feed may not function well under another one. Even within the same type feed, the compositions may vary large enough in continuous operating that requires online operating condition tuning. Currently, two types of control strategies are applied to address the issue: one is feedforward control that the feed composition is measured in advance so that the operating conditions could be set accordingly. However, the performance would rely on the accuracy of the process model, and may not be robust on feed composition disturbances. The other is simple PID feedback control that the conditions are adjusted by the product composition feedback. However, the complicity of the multi-input multi-output (MIMO) steam reforming may bring challenges to the PID controller design.
Student Scope of Work: A Bayesian-based model-set management method will be developed to construct a statistically superior model set for on-line model-based control applications. In this management method, a number of steam reforming models are developed on various feed types or compositions. Then the optimal operating conditions are identified based on the weighted model predictions. During on-line application, the measured product compositions are feedback to adjust the weights by a Bayesian-based statistical method. A repeated use of the method keeps the weights updated constantly based on the newly available system data, which makes the model-set-based prediction more precise, robust, and adaptive to various feeds automatically. The efficacy of the method will be evaluated by studying a steam reforming system with different feeds.
Social Equity and Environmental Justice: Exploration of Spatio-Temporal Traits and Influential Factors of various Air Pollutants
CPP advisors: Wen Cheng and Yongping Zhang
UoL Advisors: Zia Wadud and Karen Lucas
This study intends to explore various influential factors of primary air pollutants (such as ozone, PM2.5, PM10, etc.) while accounting for the associated space-temporal correlations. Alternate models of interest will be developed to incorporate different correlation structures: 1) univariate model which serves as reference for comparison; 2) univariate spatial or temporal model which incorporates the spatial/temporal random effects to account for the correlation structures among the Traffic Analysis Zones or other spatial units; 3) multivariate model which addresses the potential correlation among the dependent variables and allowed the simultaneous prediction to generate more precise estimates, and 4) semiparametric model which takes advantage of Dirichlet mixture.
Student scope of work: The student will collect data, including household density, population, education, and poverty, and so on, which portray the disproportionate impact of different air pollutants on the specific areas which require the efforts to emphasize social equity and environmental justice. In terms of factors pertaining to traffic conditions, traffic volume of different types of vehicles, mileage of roadway network; pedestrian and bicycle facilities, etc. will also be obtained. The student will then develop models to illustrate the factors which would significantly impact air pollutants. In addition, the amount of variability explained by spatial and/or serial random effects will be revealed. With respect to model performance at goodness-of-fit, both cross validation and predication based on in-sample data will be utilized.
An Evaluation of User Experiences in Smart Buildings
CPP Advisor: Greg Placencia, IME
UoL Advisors: Alison McKay, Matthew Davis, and Simon Rees
Digital technologies and interactive controls are widely used in the modern building management systems relied upon in advanced low-energy non-residential buildings. The design intent behind the use of these solutions is to maximize the health and wellbeing of building users while minimizing environmental impact by reducing energy consumption. Anecdotally, the design intent is often not achieved. Reported dissatisfaction indicates that user satisfaction with buildings controlled in this way is low and that users’ corrective actions increase energy consumption. However, there is little peer-reviewed scientific evidence to support this conclusion.
Student Scope of Work: In this project the student will identify implications (for user satisfaction and energy consumption) of alternative building management and control strategies (ranging from fully automated, to centrally and locally controlled by people) with a view to informing the establishment of design tools that support the consideration of alternative function allocation scenarios in design processes. The project will begin with a qualitative study of user satisfaction for case study buildings (cases to be determined), followed by a detailed quantitative analysis of a selection of buildings or rooms where energy use and user behaviors and satisfaction can be measured.
Improved Medical Implants
CPP Advisor: Yon Gan, ME
UoL Advisors: Lee Eckels and William Gale
Titanium alloy implants are commonly used in biomedical applications due to their high resistance to yield and fatigue, while providing less rigid (and therefore more bone like) fixation than stainless steels. A TiO2 nanotube coating on a titanium substrate has been shown to support cell adhesion and bony ingrowth and improve the fixation of implants, and as such has been used clinically for dental implants. Orthopedic surgeries are an extremely energy intensive process and improving implant fixation could significantly reduce the failure rate and therefore both the cost in patient quality of life and the environmental cost of both revision surgeries and reduced patient mobility. The mechanical behavior of the material, however, and in particular the coating and coating-substrate interface has not been fully characterized.
Student Scope of Work: The objectives of this project are to use the existing mechanical testing data, along with computational modelling, to design a test sample geometry to provide reliable and robust mechanical properties under testing. The samples will then need to be manufactured at CPP, and attempts made to quantify via SEM or other methods the coating variability from sample to sample. In keeping with the sustainability focus of the project the energy cost of the coating process should be calculated and extrapolated to an appropriate biomedical application. The samples would then be taken to the University of Leeds for testing and characterization and after testing the samples would be imaged once again to investigate the effect of loading on the coating structure.
Design of Microgrids in Developing Countries
Advisors: Petros Anstidou, UoL
US Advisor: Hossein Salehfar
Several developing countries around the world have a very low percentage of electrification, especially in rural areas. In some countries (like Uganda), electrification can be as low as 10%. One promising solution is the design and construction of low-cost, sustainable and resilient microgrids (small networks of electricity users with local sources of energy supply that can be attached to a centralized grid but also operate independently). The design process for these microgrids relies on difficult to solve mixed-integer optimization problems to select the optimal location and size of generators, the use and size of storage, the effectiveness of operation schemes, etc.
Student Scope of Work: This project aims to extend traditional planning optimization formulations to include new bio-energy generators (BEGs) in the microgrid design process to increase sustainability and resiliency (bio-mass can be sourced locally at a lower cost than conventional fuel). The student will develop the mathematic models and constraints relating to the BEGs and incorporate them into a decision process with the solution of Mixed-Integer Linear Problems (MILP) at the core. The existing platform is developed in MATLAB with the modelling and optimization package YALMIP.
- Applications deadline: 1 December, 2017
- Finalize interviews: 9 December, 2017
- Student selections: 31 December, 2017
- Begin Spring activities: week of January 15, 2018
- Travel to Leeds: 1 July, 2018
- End Leeds program: 24 Aug, 2018
- Begin Fall activities: 24 Sep, 2018