Impact de la récolte sur la survie et la croissance de deux plantes médicinales cries, Sarracenia purpurea et Rhododendron groenlandicum, dans le Nord du Québec

Au cours des dernières décennies, la popularité des plantes médicinales s’est accrue auprès des civilisations occidentales de sorte que la quantité de plantes récoltées, la plupart provenant de populations sauvages, a grandement augmenté. Dans ce contexte, l’objectif principal de mon mémoire est d’évaluer l’impact de la récolte de deux plantes médicinales (Sarracenia purpurea et Rhododendron groenlandicum) utilisées par la Nation Crie du Nord du Québec. Pour y parvenir, des parcelles expérimentales, simulant différentes intensités de récolte (S. purpurea) et différentes méthodes de récolte (R. groenlandicum), ont été mises en place, puis des suivis annuels de la reprise ont été réalisés. Les résultats obtenus suggèrent que les techniques de récolte chez R. groenlandicum devraient exclure les nouvelles pousses, leur exploitation causant une forte mortalité. Par ailleurs, chez S. purpurea, la récolte de 20 % des individus semble peu dommageable, mais critique lorsque plus de 50 % des plants sont récoltés. Un modèle démographique pour S. purpurea a aussi été construit à partir des observations de terrain. Ce modèle a permis de réaliser des projections temporelles en variant les taux de récoltes ainsi que les intervalles entre les récoltes. Les résultats indiquent qu’une récolte de 20 % des individus est acceptable une fois tous les 20 ans. Pour une récolte plus régulière, 5 % tous les trois ans serait soutenable. Mon projet permettra d’assurer une exploitation soutenable de deux plantes médicinales ayant un grand potentiel pour le traitement du diabète de type II.

Process modelling and simulation of tissue damage during mechanical peeling of pumpkin as a tough skinned vegetable

Food waste is a current challenge that both developing and developed countries face. This project applied a novel combination of available methods in Mechanical, agricultural and food engineering to address these challenges. A systematic approach was devised to investigate possibilities of reducing food waste and increasing the efficiency of industry by applying engineering concepts and theories including experimental, mathematical and computational modelling methods. This study highlights the impact of comprehensive understanding of agricultural and food material response to the mechanical operations and its direct relation to the volume of food wasted globally.

Silvicultural decisions based on simulation-optimization systems

Forest management is facing new challenges under climate change. By adjusting thinning regimes, conventional forest management can be adapted to various objectives of utilization of forest resources, such as wood quality, forest bioenergy, and carbon sequestration. This thesis aims to develop and apply a simulation-optimization system as a tool for an interdisciplinary understanding of the interactions between wood science, forest ecology, and forest economics. In this thesis, the OptiFor software was developed for forest resources management. The OptiFor simulation-optimization system integrated the process-based growth model PipeQual, wood quality models, biomass production and carbon emission models, as well as energy wood and commercial logging models into a single optimization model. Osyczka s direct and random search algorithm was employed to identify optimal values for a set of decision variables. The numerical studies in this thesis broadened our current knowledge and understanding of the relationships between wood science, forest ecology, and forest economics. The results for timber production show that optimal thinning regimes depend on site quality and initial stand characteristics. Taking wood properties into account, our results show that increasing the intensity of thinning resulted in lower wood density and shorter fibers. The addition of nutrients accelerated volume growth, but lowered wood quality for Norway spruce. Integrating energy wood harvesting into conventional forest management showed that conventional forest management without energy wood harvesting was still superior in sparse stands of Scots pine. Energy wood from pre-commercial thinning turned out to be optimal for dense stands. When carbon balance is taken into account, our results show that changing carbon assessment methods leads to very different optimal thinning regimes and average carbon stocks. Raising the carbon price resulted in longer rotations and a higher mean annual increment, as well as a significantly higher average carbon stock over the rotation.

Power Management and Data Rate Maximization in Wireless Energy Harvesting Sensors

This paper considers the problem of power management and throughput maximization for energy neutral operation when using Energy Harvesting Sensors (EHS) to send data over wireless links. It is assumed that the EHS are designed to transmit data at a constant rate (using a fixed modulation and coding scheme) but are power-controlled. A framework under which the system designer can optimize the performance of EHS when the channel is Rayleigh fading is developed. For example, the highest average data rate that can be supported over a Rayleigh fading channel given the energy harvesting capability, the battery power storage efficiency and the maximum allowed transmit energy per slot is derived. Furthermore, the optimum transmission scheme that guarantees a particular data throughput is derived. The usefulness of the framework developed is illustrated through simulation results for specific examples.

Transmit Power Control Policies for Energy Harvesting Sensors With Retransmissions

This paper addresses the problem of finding outage-optimal power control policies for wireless energy harvesting sensor (EHS) nodes with automatic repeat request (ARQ)-based packet transmissions. The power control policy of the EHS specifies the transmission power for each packet transmission attempt, based on all the information available at the EHS. In particular, the acknowledgement (ACK) or negative acknowledgement (NACK) messages received provide the EHS with partial information about the channel state. We solve the problem of finding an optimal power control policy by casting it as a partially observable Markov decision process (POMDP). We study the structure of the optimal power policy in two ways. First, for the special case of binary power levels at the EHS, we show that the optimal policy for the underlying Markov decision process (MDP) when the channel state is observable is a threshold policy in the battery state. Second, we benchmark the performance of the EHS by rigorously analyzing the outage probability of a general fixed-power transmission scheme, where the EHS uses a predetermined power level at each slot within the frame. Monte Carlo simulation results illustrate the performance of the POMDP approach and verify the accuracy of the analysis. They also show that the POMDP solutions can significantly outperform conventional ad hoc approaches.

Transmit power Control with ARQ in energy harvesting sensors: a decision-theoretic approach

This paper addresses the problem of finding optimal power control policies for wireless energy harvesting sensor (EHS) nodes with automatic repeat request (ARQ)-based packet transmissions. The EHS harvests energy from the environment according to a Bernoulli process; and it is required to operate within the constraint of energy neutrality. The EHS obtains partial channel state information (CSI) at the transmitter through the link-layer ARQ protocol, via the ACK/NACK feedback messages, and uses it to adapt the transmission power for the packet (re)transmission attempts. The underlying wireless fading channel is modeled as a finite state Markov chain with known transition probabilities. Thus, the goal of the power management policy is to determine the best power setting for the current packet transmission attempt, so as to maximize a long-run expected reward such as the expected outage probability. The problem is addressed in a decision-theoretic framework by casting it as a partially observable Markov decision process (POMDP). Due to the large size of the state-space, the exact solution to the POMDP is computationally expensive. Hence, two popular approximate solutions are considered, which yield good power management policies for the transmission attempts. Monte Carlo simulation results illustrate the efficacy of the approach and show that the approximate solutions significantly outperform conventional approaches.

Optimisation of the first principle code Octopus for massive parallel architectures: application to light harvesting complexes

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Simulating fisheries for the assessment of optimum harvesting strategies

Some results of a line of research explored by the author in recent years, and concerning the small-scale fisheries of Mexico are discussed. Clarity of goals for fisheries management is stressed as a departure point before taking any step towards model building. Age-structured simulation models require input data and parameters such as growth rates, natural mortality, age at first capture and maturity, longevity, the longest possible catch records series, and estimates of numbers caught per age group. The link between each cohort and the following can then be established by means of the Ricker stock recruitment or the Beverton-Holt models. Simulation experiments can then be carried out by changing fishing mortality. Whenever data on profits and costs and catch are available, these can also be analyzed. The use of simulation models is examined with emphasis on the benefits derived from their use for fisheries management.

A model for the simulation of macroalgal population dynamics and productivity

A mathematical model to simulate the population dynamics and productivity of macroalgae is described. The model calculates the biomass variation of a population divided into size-classes. Biomass variation in each class is estimated from the mass balance of carbon fixation, carbon release and demographic processes such as mortality and frond breakage. The transitions between the different classes are calculated in biomass and density units as a function of algal growth. Growth is computed from biomass variations using an allometric relationship between weight and length. Gross and net primary productivity is calculated from biomass production and losses over the period of simulation. The model allows the simulation of different harvesting strategies of commercially important species. The cutting size and harvesting period may be changed in order to optimise the calculated yields. The model was used with the agarophyte Gelidium sesquipedale (Clem.) Born. et Thur. This species was chosen because of its economic importance as a the main raw material for the agar industry. Net primary productivity calculated with it and from biomass variations over a yearly period, gave similar results. The results obtained suggest that biomass dynamics and productivity are more sensitive to the light extinction coefficient than to the initial biomass conditions for the model. Model results also suggest that biomass losses due to respiration and exudation are comparable to those resulting from mortality and frond breakage. During winter, a significant part of the simulated population has a negative net productivity. The importance of considering different parameters in the productivity light relationships in order to account for their seasonal variability is demonstrated with the model results. The model was implemented following an object oriented programming approach. The programming methodology allows a fast adaptation of the model to other species without major software development.

Energy harvesting system design and optimization for wireless sensor networks

Wireless sensor networks (WSN) are becoming widely adopted for many applications including complicated tasks like building energy management. However, one major concern for WSN technologies is the short lifetime and high maintenance cost due to the limited battery energy. One of the solutions is to scavenge ambient energy, which is then rectified to power the WSN. The objective of this thesis was to investigate the feasibility of an ultra-low energy consumption power management system suitable for harvesting sub-mW photovoltaic and thermoelectric energy to power WSNs. To achieve this goal, energy harvesting system architectures have been analyzed. Detailed analysis of energy storage units (ESU) have led to an innovative ESU solution for the target applications. Battery-less, long-lifetime ESU and its associated power management circuitry, including fast-charge circuit, self-start circuit, output voltage regulation circuit and hybrid ESU, using a combination of super-capacitor and thin film battery, were developed to achieve continuous operation of energy harvester. Low start-up voltage DC/DC converters have been developed for 1mW level thermoelectric energy harvesting. The novel method of altering thermoelectric generator (TEG) configuration in order to match impedance has been verified in this work. Novel maximum power point tracking (MPPT) circuits, exploring the fractional open circuit voltage method, were particularly developed to suit the sub-1mW photovoltaic energy harvesting applications. The MPPT energy model has been developed and verified against both SPICE simulation and implemented prototypes. Both indoor light and thermoelectric energy harvesting methods proposed in this thesis have been implemented into prototype devices. The improved indoor light energy harvester prototype demonstrates 81% MPPT conversion efficiency with 0.5mW input power. This important improvement makes light energy harvesting from small energy sources (i.e. credit card size solar panel in 500lux indoor lighting conditions) a feasible approach. The 50mm × 54mm thermoelectric energy harvester prototype generates 0.95mW when placed on a 60oC heat source with 28% conversion efficiency. Both prototypes can be used to continuously power WSN for building energy management applications in typical office building environment. In addition to the hardware development, a comprehensive system energy model has been developed. This system energy model not only can be used to predict the available and consumed energy based on real-world ambient conditions, but also can be employed to optimize the system design and configuration. This energy model has been verified by indoor photovoltaic energy harvesting system prototypes in long-term deployed experiments.

A microwave metamaterial with integrated power harvesting functionality

We present the design and experimental implementation of a power harvesting metamaterial. A maximum of 36.8% of the incident power from a 900 MHz signal is experimentally rectified by an array of metamaterial unit cells. We demonstrate that the maximum harvested power occurs for a resistive load close to 70 Ω in both simulation and experiment. The power harvesting metamaterial is an example of a functional metamaterial that may be suitable for a wide variety of applications that require power delivery to any active components integrated into the metamaterial. © 2013 AIP Publishing LLC.

Equipping Simulation Model (BIOSIM)’s Actors With Multi-agent Intelligence on Cross platforms

Thesis is to Introduce an Intelligent cross platform architecture with Multi-agent system in order to equip the simulation Models with agents, having intelligent behavior, reactive and pro-active nature and rational in decision making.

On the optimum parameters of a device for harvesting energy from running and walking

This paper investigates the feasibility of using an energy harvesting device tuned such that its natural frequency coincides with higher harmonics of the input to capture energy from walking or running human motion more efficiently. The paper starts by reviewing the concept of a linear resonant generator for a tonal frequency input and then derives an expression for the power harvested for an input with several harmonics. The amount of power harvested is estimated numerically using measured data from human subjects. Assuming that the input is periodic, the signal is reconstructed using a Fourier series before being used in the simulation. It is found that although the power output depends on the input frequency, the choice of tuning the natural frequency of the device to coincide with a particular higher harmonic is restricted by the amount of damping that is needed to maximize the amount of power harvested, as well as to comply with the size limit of the device. It is also found that it is not feasible to tune the device to match the first few harmonics when the size of the device is small, because a large amount of damping is required to limit the motion of the mass.

Development of an optimization model for biofuel facility size and location and a simulation model for design of a biofuel supply chain

To mitigate greenhouse gas (GHG) emissions and reduce U.S. dependence on imported oil, the United States (U.S.) is pursuing several options to create biofuels from renewable woody biomass (hereafter referred to as “biomass”). Because of the distributed nature of biomass feedstock, the cost and complexity of biomass recovery operations has significant challenges that hinder increased biomass utilization for energy production. To facilitate the exploration of a wide variety of conditions that promise profitable biomass utilization and tapping unused forest residues, it is proposed to develop biofuel supply chain models based on optimization and simulation approaches. The biofuel supply chain is structured around four components: biofuel facility locations and sizes, biomass harvesting/forwarding, transportation, and storage. A Geographic Information System (GIS) based approach is proposed as a first step for selecting potential facility locations for biofuel production from forest biomass based on a set of evaluation criteria, such as accessibility to biomass, railway/road transportation network, water body and workforce. The development of optimization and simulation models is also proposed. The results of the models will be used to determine (1) the number, location, and size of the biofuel facilities, and (2) the amounts of biomass to be transported between the harvesting areas and the biofuel facilities over a 20-year timeframe. The multi-criteria objective is to minimize the weighted sum of the delivered feedstock cost, energy consumption, and GHG emissions simultaneously. Finally, a series of sensitivity analyses will be conducted to identify the sensitivity of the decisions, such as the optimal site selected for the biofuel facility, to changes in influential parameters, such as biomass availability and transportation fuel price. Intellectual Merit The proposed research will facilitate the exploration of a wide variety of conditions that promise profitable biomass utilization in the renewable biofuel industry. The GIS-based facility location analysis considers a series of factors which have not been considered simultaneously in previous research. Location analysis is critical to the financial success of producing biofuel. The modeling of woody biomass supply chains using both optimization and simulation, combing with the GIS-based approach as a precursor, have not been done to date. The optimization and simulation models can help to ensure the economic and environmental viability and sustainability of the entire biofuel supply chain at both the strategic design level and the operational planning level. Broader Impacts The proposed models for biorefineries can be applied to other types of manufacturing or processing operations using biomass. This is because the biomass feedstock supply chain is similar, if not the same, for biorefineries, biomass fired or co-fired power plants, or torrefaction/pelletization operations. Additionally, the research results of this research will continue to be disseminated internationally through publications in journals, such as Biomass and Bioenergy, and Renewable Energy, and presentations at conferences, such as the 2011 Industrial Engineering Research Conference. For example, part of the research work related to biofuel facility identification has been published: Zhang, Johnson and Sutherland [2011] (see Appendix A). There will also be opportunities for the Michigan Tech campus community to learn about the research through the Sustainable Future Institute.

DEVELOPMENT OF AN OPTIMIZATION MODEL FOR BIOFUEL FACILITY SIZE AND LOCATION AND A SIMULATION MODEL FOR DESIGN OF A BIOFUEL SUPPLY CHAIN

A range of societal issues have been caused by fossil fuel consumption in the transportation sector in the United States (U.S.), including health related air pollution, climate change, the dependence on imported oil, and other oil related national security concerns. Biofuels production from various lignocellulosic biomass types such as wood, forest residues, and agriculture residues have the potential to replace a substantial portion of the total fossil fuel consumption. This research focuses on locating biofuel facilities and designing the biofuel supply chain to minimize the overall cost. For this purpose an integrated methodology was proposed by combining the GIS technology with simulation and optimization modeling methods. The GIS based methodology was used as a precursor for selecting biofuel facility locations by employing a series of decision factors. The resulted candidate sites for biofuel production served as inputs for simulation and optimization modeling. As a precursor to simulation or optimization modeling, the GIS-based methodology was used to preselect potential biofuel facility locations for biofuel production from forest biomass. Candidate locations were selected based on a set of evaluation criteria, including: county boundaries, a railroad transportation network, a state/federal road transportation network, water body (rivers, lakes, etc.) dispersion, city and village dispersion, a population census, biomass production, and no co-location with co-fired power plants. The simulation and optimization models were built around key supply activities including biomass harvesting/forwarding, transportation and storage. The built onsite storage served for spring breakup period where road restrictions were in place and truck transportation on certain roads was limited. Both models were evaluated using multiple performance indicators, including cost (consisting of the delivered feedstock cost, and inventory holding cost), energy consumption, and GHG emissions. The impact of energy consumption and GHG emissions were expressed in monetary terms to keep consistent with cost. Compared with the optimization model, the simulation model represents a more dynamic look at a 20-year operation by considering the impacts associated with building inventory at the biorefinery to address the limited availability of biomass feedstock during the spring breakup period. The number of trucks required per day was estimated and the inventory level all year around was tracked. Through the exchange of information across different procedures (harvesting, transportation, and biomass feedstock processing procedures), a smooth flow of biomass from harvesting areas to a biofuel facility was implemented. The optimization model was developed to address issues related to locating multiple biofuel facilities simultaneously. The size of the potential biofuel facility is set up with an upper bound of 50 MGY and a lower bound of 30 MGY. The optimization model is a static, Mathematical Programming Language (MPL)-based application which allows for sensitivity analysis by changing inputs to evaluate different scenarios. It was found that annual biofuel demand and biomass availability impacts the optimal results of biofuel facility locations and sizes.