Maximize System Efficiency Research Articles

Implementation of thermoelectric wall systems for sustainable indoor environment regulation in buildings through numerical and experimental performance analysis

With buildings representing a substantial portion of global energy consumption, exploring alternatives to traditional fossil fuel-based heating and cooling systems is critical. Thermoelectricity offers a promising solution by converting temperature differentials into electrical voltage or vice versa, enabling efficient indoor thermal regulation. This paper presents a comprehensive investigation into the integration of thermoelectric wall systems for sustainable building climate control through numerical simulations and experimental analyses. Numerical simulations using computational fluid dynamics (CFD) techniques were conducted to model fluid flow and heat transfer within the thermoelectric wall systems under various operating conditions. These simulations provided insights into the system’s thermal behavior, which were validated through experimental setups designed to measure temperature differentials, airflow rates, and power consumption. The results showed that power consumption is directly correlated with electrical current, ranging from 0.19 W to 77.4 W as the current increased from 0.1 A to 2 A. Additionally, the heat absorbed by the system increased significantly with electrical current, by 706–1044%, depending on the air velocity. The thermal energy released from the hot side of the thermoelectric modules also rose substantially, ranging from 9850 to 5285% with increasing electrical current, and from 275 to 51% with higher air velocities. Moreover, increasing air velocity led to a 6.78–9.37% reduction in power consumption for currents between 0.1 A and 2 A. The coefficient of performance (COP) analysis revealed that optimizing both electrical current and air velocity is essential for maximizing system efficiency. While fan power consumption reduces COP at higher air velocities, neglecting fan power consumption results in COP improvements ranging from 6.5 × 10⁻⁴ to 49.0%. These findings highlight the potential of thermoelectric wall systems to enhance indoor comfort and energy efficiency in buildings.

Characterization of Enterobacter cloacae CAUCoF_BF_01: an autochthonous heterotrophic nitrifying strain from culture ponds for biofloc applications

The biofloc environment needs specific heterotrophic nitrifying bacteria for optimal system performance, hence screening perfect bacterial strains is crucial. Enterobacter cloacae sp. was identified as a novel heterotrophic bacterium isolated from freshwater pond sediment during July, 2022. Through high-throughput sequencing, the isolated bacterium was identified as E. cloacae CAUCoF_BF_01, which demonstrated potential nitrogen removal via heterotrophic nitrification and aerobic denitrification, as well as a preference for different C/N ratios (C/N10, 15, 20, and 25) for consuming nitrogen sources. The bacteria performed superior (p < 0.05) ammonia-N and nitrite-N removal in C/N20, with efficiencies of 91.70% and 91.0%, respectively. C/N15, on the other hand, had higher nitrate-N removal efficiency (69.20%). Furthermore, the optimum temperature for the chosen strain was determined to be 30 °C. This strain, on the other hand, can thrive in temperatures ranging from 20 to 37 °C and pH levels ranging from 6 to 10. Furthermore, the biofilm test reveals that the selected bacterial strain has the potential to form biofilm via extracellular polymeric compound secretion, besides passing ecological safety requirement. The current investigation will be helpful in the development of a successful biofloc-based probiotic for managing system generated ammonia to an optimal level, as well as floc production to maximize system efficiency.

Finding robustly fair solutions in resource allocation

In this study, we consider resource allocation settings where the decisions affect multiple beneficiaries and the decision maker aims to ensure that the effect is distributed to the beneficiaries in an equitable manner. We specifically consider stochastic environments where there is uncertainty in the system and propose a robust programming approach that aims at maximizing system efficiency while guaranteeing an equitable benefit allocation even under the worst scenario. Acknowledging the fact that the robust solution may lead to high efficiency loss and may be over-conservative, we adopt a parametric approach that allows controlling the level of conservatism and present the decision maker alternative solutions that reveal the trade-off between efficiency and the degree of conservatism when incorporating fairness. We obtain tractable formulations, leveraging the results we provide on the properties of highly unfair allocations. We demonstrate the usability of our approach on project selection and shelter allocation applications.

An inclined magnetic field and slip effect on heat and mass transfer analysis in the peristaltic flow of immiscible fluid through an asymmetric porous channel

In this work, authors have studied peristaltic flow of immiscible type of couple stress and Newtonian fluid via an asymmetric horizontal channel filled with porous material by taking slippage into account at the channel wall which was ignored in previous published literature under the influence of thermal radiation and magnetic field. The study of peristaltic flow of considered immiscible fluids will play an important role in maximizing system efficiency, reducing energy losses, and improving performance of various fluid transport. In the present problem, the linearized form of governing equations has been solved by direct method with the use of Mathematica 14 software to obtained the closed form solutions of various flow variables. In this work, the impacts of various influencing factors on the model's flow, thermal, and concentration properties have been analyzed graphically. The work presented here leads to the significant result that large Schmidt number values in a porous filled duct reduces the mass diffusivity. Another key finding of this work is that the formation of entropy is reduced when low permeability porous material is used. Our research concludes that entropy creation in the asymmetric porous channel is decreased when the slip length increases on the channel's static borders.

A particle swarm optimization inspired global and local stability driven predictive load balancing strategy

In distributed systems and parallel computing, optimal load balancing is difficult. These abstract addresses load balancing in distributed situations, highlighting current solutions' flaws and emphasizing the need for new ones. Load balancing research includes centralized and distributed algorithms, heuristics, and predictive models. Despite various successful methods, workload adaptability, overhead reduction, and scaling to large systems remain unresolved. This study proposes a particle swarm optimization (PSO) load balancing method that considers global and local stability considerations. The proposed method uses PSO principles to balance exploration and exploitation and allocate resources among distributed nodes. Predictive components improve preventative load management by predicting workload changes. Global and local load balancing stability criteria distinguish this study. The recommended method considers global system-wide performance indicators, local node-level characteristics, and micro-level stability to maximize system efficiency. A dual-focus technique distinguishes the proposed load balancing strategy from others, solving dynamic distributed system challenges. The study examines load balancing system advances and suggests improvements and further research. More accurate prediction modeling, stability measures, and application-specific enhancements may be studied in the future. Experimental validation and real-world implementation of the recommended approach are necessary to determine its practicality and ability to handle modern distributed computing systems.

Optimal design and current control strategies of an electrodynamic tape for ISS station-keeping

This study analyzes the performance of a tape-like bare electrodynamic tether as a promising propellant-free technology for the International Space Station (ISS) station-keeping, supporting the concept that the technology can provide significant mission benefits by reducing the ISS reliance on costly refueling operations for orbit maintenance. Convenient control laws for managing the electrical power supplied to the tether are proposed, exploring two distinct scenarios. The first involves using the electrodynamic tether continuously to counteract aerodynamic drag. The second adopts a cyclic approach, alternating between boosting the station with the tether and allowing for periods of natural decay. Optimal tether geometry, aimed at maximizing system efficiency, is also detailed. The study specifies an electrodynamic tether configuration featuring a 6-kilometer-long aluminum ribbon, 5 cm wide and 50 μm thick, capable of overcoming aerodynamic drag ranging from 0.40 N to 0.80 N. Additionally, numerical simulations assess the tether performance under real environmental conditions. Furthermore, the study briefly introduces the potential of a photovoltaic tether as a fully autonomous system capable of supplying the necessary input power.

End-to-end deep learning for a flexible coherent PON with user-specific constellation optimization

A flexible coherent passive optical network (FLCS-CPON) is a promising solution for the future access network. By offering increased speed, sensitivity, and flexibility, it enables more efficient utilization of network resources and allows for serving a larger number of users. However, the past studies often overlook the flexibility of channel equalization. In the FLCS-CPON, customized rate optimization has been achieved to cater to users in different channel conditions. However, in addition to rate optimization, further performance improvement can be achieved by providing customized equalization methods. In this work, we proposed and demonstrated an end-to-end (E2E)-optimization-based FLCS-CPON in a 50 km fiber transmission. It enables tailored signal constellation shaping and equalization, maximizing system efficiency and performance. Finally, we achieved a FLCS-CPON with the net data rate (NDR) varied from 124 to 210 Gbps and the power budget of 40 and 42.4 dB in upstream and downstream, respectively; 3.7 and 2.9 dB power budgets are improved by E2E optimization. In burst-mode, the dynamic range of probabilistic shaping 32 quadrature amplitude modulation (PS-32QAM) at a line rate of 250 Gbps improved by 6.1 to 16.8 dB. Additionally, a dynamic range and net-rate product (DRNRP) of 5779 dB · Gbps is achieved.

Experimental investigation on the performance of a micro-ORC system for different operating conditions

The micro-ORC systems are widely considered a reliable solution for domestic power production from renewable sources. The investigation of the optimal operating conditions to maximize system efficiency is an interesting challenge. In this study, a preliminary experimental campaign has been carried out on a biomass-fired micro-ORC system. The system is designed for stationary applications for domestic users, with a gear pump, a scroll expander and R245fa as the working fluid. The performance characterization of the micro-ORC under steady-state conditions has been obtained varying the water flow rate in the condenser at constant pump and expander speeds. The temperature of the hot source (thermal oil) is the maximum achievable in each operating condition. The temperature at the expander inlet and the condenser and evaporator pressure strongly influence the system performance. The increase in water flow leads to a decrease in the condenser pressure and a reduction of the superheating degree of the organic fluid. The system reaches the maximum electric power output of approximately 2565 W with a water flow rate of about 20 l/min. The highest electrical efficiency increases as the refrigerant flow rate decreases and reaches the highest value of 8.1% for the minimum investigated water flow rate.

Improving efficiency of parallel inverters operation in island mode microgrids

DC/AC inverters play a vital role in microgrids, efficiently converting renewable energy into usable AC power. Parallel operation of inverters presented numerous challenges, including maximizing system efficiency, minimizing circulating current, and maximizing system accuracy. This proposal introduces an analytical optimization technique designed to enhance the efficiency of paralleled inverters in microgrid systems while minimizing circulating current. The system parameter estimation is performed with a rapid recursive least squares (RLS) estimator. An optimized proportional-integral-derivative (PID) controller achieves high accuracy and streamlining system construction. The performance of the proposed optimizer is compared to common optimization methods, such as particle swarm optimization (PSO), neural networks, interior search, and interior point optimizers, focusing on system efficiency and eliminating circulating currents. Simulation investigations validated the method's applicability and demonstrated the proposed optimizer's superiority in efficiency, stability, and limiting circulating currents. It also achieved zero execution time, significantly outperforming alternatives like the neural network optimizer, which took 0.693 s. In various scenarios, the proposed optimizer improved system efficiency by 3% compared to the equally shared current system.

Research on optimal operation of cascade pumping stations based on an improved sparrow search algorithm.

For the low efficiency and large loss of cascade pumping stations, aiming to maximize system efficiency, an optimized scheduling model of cascade pumping stations is established with consideration of multiple constraints, and the optimal scheduling method based on the improved sparrow search algorithm (BSSA) is proposed. The BSSA is initialized by the Bernoulli chaotic map to solve the insufficient initial diversity of the sparrow search algorithm (SSA). The random boundary strategy is introduced to avoid local optimum when dealing with the scheduling problem of pumping stations. The performance and improvement strategy of BSSA are verified by eight benchmark functions. Results show that BSSA has better convergence accuracy and faster speed. BSSA is applied to a three-stage pumping station considering three flow conditions, and compared with the current scheme, particle swarm optimization and genetic algorithm optimization schemes, the operation efficiency of SSA can be increased by 0.72-0.96%, and operation cost can be reduced by ¥263,000/a-¥363,300/a. On this basis, the improvement of 0.04-0.30% and ¥14,800/a-¥109,900/a can be further achieved by the BSSA, which confirms the feasibility and effectiveness of BSSA to solve the pumping station optimal scheduling problem. The findings present useful reference for the optimized scheduling of pumping station system.

Configurations and Operation Methods of Diesel-Based Solid Oxide Fuel Cell System

Solid oxide fuel cell(SOFC) is a recently developed technology which has attracted increasing attention for its high efficiency and low emission. While the production and delivery of hydrogen still have giant obstacles, diesel will be a good substitute for its high heating value and low cost. However, it has not been widely used as the fuel for SOFC and researches on diesel-based SOFC system still remains to be further explored. In this work, a diesel steam reforming(SR)-SOFC system is studied. We proposed a series of system configurations of diesel-based SOFC and fitted key system units based on measured data of reforming and stack experiments. Moreover, the best system operation condition intervals were presented to achieve the maximize system efficiency and heat balance. The results suggest that a system with anode off-gas recirculation and electrodes off-gas mixture has the highest efficiency because it has higher syngas heat value and lower off-gas temperature; by adding a water condenser between reformer and SOFC stack, the system efficiency can be improved for higher percentage of H2 in syngas although the condenser will take away a part of the heat. Under this system configuration and the best system operation condition interval , the system efficiency can reach 58.2%(the stack efficiency is 45.8%), improving approximately 10% comparing with the system without anode off-gas recirculation. This research reveals the efficiency variance between different system configurations and gives a better system configuration as well as preferred operation methods, which lays a theoretical foundation for the upcoming design of a 100kw class physical system.Keywords: solid oxide fuel cell, diesel reforming, system configurations, operation method

Efficient Control of a Switched Inertance Hydraulic Converter With a Time-Varying Load

Abstract Digital hydraulics is a novel alternative to proportional or servovalve-controlled systems in fluid power engineering, providing hydraulic systems with high-energy efficiency, good controllability, and insensitivity to contamination. Switched inertance hydraulic converters (SIHCs) are new digital hydraulic devices that can adjust flow and pressure by digital switching instead of throttling the flow. In this paper, an efficient closed-loop control system is proposed for SIHCs subject to time-varying loading conditions in which the load pressure and/or flow varies with time. The control system is designed to operate SIHCs at optimized switching frequencies and ratios that maximize system efficiency when the load varies. With the proposed controller, the SIHC can effectively adapt to the time-varying load and has achieved up to 10% efficiency improvement and up to 65% pressure ripple reduction without affecting the system's dynamic responses, compared with using a nonoptimized controller. The work shows the feasibility and advantages of simultaneously controlling the switching ratio and switching frequency of SIHCs with a time-varying load. As time-varying loading conditions are commonly found in hydraulic applications, the research outcomes constitute an important aspect in the design and development of highly efficient SIHCs and their practical use in hydraulic machinery.

A multi-objective optimization in system level for thermoelectric generation system

For designing thermoelectric generation systems in practice, achieving the ideal goal of maximizing output power, maximizing system efficiency, and minimizing system investment are important. However, assumptions about the input heat and some coefficients considered by existing studies are usually ideal. The optimization of thermoelectric generator (TEG) systems by existing studies is usually for output power and conversion efficiency. In addition, existing studies are more focused on the optimization of TEG geometry rather than that of system parameters. The comprehensive multi-objective optimization of the overall system is lacking, as well as the comprehensive study of effects of system parameters on system performance. To help with the design of TEG system in real applications, this study firstly proposes a transient model for TEG system. The inputs are no longer unlimited heat source, instead, the amplitude, frequency and heat transfer coefficient are three system parameters considered. The model is then used to explore effects of three system parameters on output power, conversion efficiency and system investment, and is used to have a multi-objective optimization for better system design. With the help of this study, the investment of the system can be lowered by 30%–50%, while only lose output power and efficiency within 1%.

ВИРІШЕННЯ РЕГРЕСІЙНОЇ ПРОБЛЕМИ ДЕМОДУЛЯЦІЇ СИГНАЛІВ З АМПЛІТУДНОЮ МОДУЛЯЦІЄЮ БАГАТЬОХ СКЛАДОВИХ

The article is devoted to the consideration of AMBS features, highlighting the methodology of AMBS signal calculation for presentation in the form of a signal constellation and time graphs, and the use of calculated signals as input data for training a neural network that performs the task of signal demodulation. To represent sets of random values of different symbols of AMBS signals, a method was proposed, the essence of which is the use of Voronoi cells as a way of dividing the space between the points of the signal constellation, which is more efficient from a geometric point of view, compared to how signals are represented in trivial information transmission systems. The theoretical increase in the efficiency of the proposed method was calculated in comparison with the trivial approach assuming a higher efficiency of Voronoi cells as a way to divide the space between points. The described methods and techniques were embodied in the algorithm of the software product, which performs the task of forming the AMBS constellation, creating noisy variations of the signal around the points, recording these variations in a file, which is later used in the training of the neural network. The principle of operation of the software product based on previously formed algorithms is described, the algorithms themselves are described, their effectiveness is evaluated, the design decisions of the software product structure are explained, in particular, attention is paid to flexibility and the possibility of adjustment for specific cases. It is described with what data and in what form the created system operates. The efficiency of the created system was evaluated using relatively high values of added noise in the analyzed signal. Conclusions are drawn regarding ways to maximize system efficiency, and the dependence of accuracy on various model parameters is depicted. The algorithm for assessing the accuracy of the prediction of the neural network was formed, implemented in the form of a subroutine of the software product, the accuracy of the proposed system was evaluated, and conclusions were drawn about the work done.

Optimization of a near-zero-emission energy system for the production of desalinated water and cooling using waste energy of fuel cells

In the present study, a biomass-based multi-purpose energy system that can generate power, desalinated water, hydrogen, and ammonia is presented. The gasification cycle, gas turbine, Rankine cycle, PEM electrolyzer, ammonia production cycle using the Haber-Bosch process, and MSF water desalination cycle are the primary subsystems of this power plant. On the suggested system, a thorough thermodynamic and thermoeconomic evaluation has been conducted. For the analysis, the system is first modeled and investigated from an energy point of view, after which it is similarly studied from an exergy point of view before the system is subjected to economic analysis (exergoeconomic analysis). The system is evaluated and modeled using artificial intelligence to aid in the system optimization process after energy, exergy, and economic modeling and analysis. The resulting model is then optimized using a genetic algorithm to maximize system efficiency and reduce system expenses. EES software does the first analysis. After that, it sends the data to MATLAB program for optimization and to see how operational factors affect thermodynamic performance and overall cost rate. To find the best solution with the maximum energy efficiency and lowest total cost, multi-objective optimization is used. In order to shorten computation time and speed up optimization, the artificial neural network acts as a middleman in the process. In order to identify the energy system's optimal point, the link between the objective function and the choice factors has been examined. The results show that increasing the flow of biomass enhances efficiency, output, and cost while raising the temperature of the gas turbine's input decreases cost while simultaneously boosting efficiency. Additionally, according to the system's optimization results, the power plant's cost and energy efficiency are 37% and 0.3950$/s, respectively, at the ideal point. The cycle's output is estimated at 18900 kW at this stage.

Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems

The need for energy has significantly increased in the world in recent years. Various research works were presented to develop Renewable Energy Sources (RESs) as green energy Distributed Generations (DGs) to satisfy this demand. In addition, alleviating environmental problems caused by utilizing conventional power plants is diminished by these renewable sources. The optimal location and size of the DG-RESs significantly affect the performance of Radial Distribution Systems (RDSs) through the fine bus voltage profile, senior power quality, low power losses, and high efficiency. This paper investigates the use of PV (photovoltaic) and (Wind Turbine) WT systems as a DG source in RDSs. This investigation is presented via the optimal location and size of the PV and WT systems, which are the most used DG sources. This optimization problem aims to maximize system efficiency by minimizing power losses and improving both voltage profile and power quality using White Shark Optimization (WSO). This algorithm emulates the attitude of great white sharks when foraging using their senses of hearing and smell. It confirms the balance between exploration and exploitation to discover optimization that is considered as the main advantage of this approach in attaining the global minimum. To assess the suggested approach, three common RDSs are utilized, namely, IEEE 33, 69, and 85 node systems. The results prove that the applied WSO approach can find the best location and size of the RESs to reduce power loss, ameliorate the voltage profile, and outlast other recent strategies. Adding more units provides a high percentage of reducing losses by at least 93.52% in case of WTs, rather than 52.267% in the case of PVs. Additionally, the annual saving increased to USD 74,371.97, USD 82,127.257, and USD 86,731.16 with PV penetration, while it reached USD 104,872.96, USD 116,136.57, and USD 155,184.893 with WT penetration for the 33, 69, and 85 nodes, respectively. In addition, a considerable enhancement in the voltage profiles with the growth of PV and WT units was confirmed. The ability of the suggested WSO for feasible implementation was validated and inspected by preserving the restrictions and working constraints.

Power Management Control of an Autonomous Photovoltaic/Wind Turbine/Battery System

The study presents an optimal control approach for managing a hybrid Photovoltaic/Wind Turbine/Battery system in an isolated area. The system includes multiple energy sources connected to a DC bus through DC/DC converters for maximum power point tracking. The proposed hybrid MPPT approach (HMPPT) manages the energy production from different sources, while the power flow method is used to balance the load and renewable power. The study shows that integrating the HMPPT algorithm and power flow approach results in improved system performance, including increased power generation and reduced stress on the batteries. The study also proposes an accurate sizing method to further improve system efficiency. The study demonstrates the effectiveness of the proposed approach by presenting results for twelve different days with varying weather conditions. The results show that the proposed approach effectively manages the energy production and load, resulting in optimal system performance. This study provides valuable insights into the optimal control of hybrid renewable energy systems, and highlights the importance of considering different energy sources and optimal sizing for maximizing system efficiency.

Optimal techno-economic multi-level energy management of renewable-based DC microgrid for commercial buildings applications

An optimal techno-economic multi-level energy management strategy (EMS) is designed for a DC microgrid applied to a commercial building. The proposed power system is a grid-connected microgrid composed of a fuel cell (FC) system, a photovoltaic (PV) array, and a battery storage unit. The interaction between various types of power sources may lead to stability problems. Moreover, providing an uninterruptible and high-quality power supply, reducing operating costs, and maximizing system efficiency are essential factors. This paper presents a techno-economic EMS, one of the most significant objectives for the following years. The proposed EMS comprises two levels for minimizing the operating cost and supplying the load and another control level based on flatness control theory to improve the power quality. The first level is based on one of the following EMSs: State Machine Control (SMC), Equivalent Consumption Minimization Strategy (ECMS), and External Energy Maximization Strategy (EEMS) to improve efficiency and reduce the required power. The second level is based on the economic dispatching algorithm (EDA) to reduce the operating cost and ensure a UPS. Finally, the system performance is elaborated; the simulation results prove the superiority of the EEMS-EDA combination, where the cost-saving can be enhanced by 3.47%, and the efficiency is about 80.44%.

Development of an accumulator liquid-level estimator to enable zero-superheat control and active charge management in vapor-compression systems

Vapor-compression system efficiency increases with reduced superheat but excessively low superheat settings may cause wet compression and jeopardize reliable system operations. Accumulators are commonly used to intercept liquid refrigerant before it reaches the compressor and can also be used as a refrigerant reservoir to assist active charge management. For zero-superheat and active charge control, accurate measurement or estimation of the accumulator liquid level is required. This paper presents an accumulator liquid level estimation approach based on a nonlinear state observer combined with a gray-box dynamic model of the evaporator and accumulator. This approach affords virtual detection of the liquid level in real-time from refrigerant pressure and temperature measurements, which are readily available in modern vapor-compression systems, and supports zero-superheat and dynamic charge control to maximize system efficiency. Both numerical and experimental tests of the proposed liquid level estimation and charge control strategies were carried out with a 3-ton variable-speed heat pump. The tests have demonstrated the accuracy of the accumulator liquid level estimator and shown that the zero-superheat and active charge control strategy could improve the system efficiency by up to 10.5%.

Optimizing the delivery of genetic and advanced diagnostic testing in the province of Ontario: challenges and implications for laboratory technology assessment and management in decentralized healthcare systems

Aims The Canadian province of Ontario provides full coverage for its residents (pop.14.8 M) for hospital-based diagnostic testing. Historical governance of the healthcare system and a legacy scheme of health technology assessment (HTA) and financing has led to a suboptimal approach of adopting advanced diagnostic technology (i.e. protein expression, cytogenetic, and molecular/genetic) for guiding therapeutic decisions. The aim of this research is to explore systemic barriers and provide guidance to improve patient and care provider experiences by reducing delays and inequity of access to testing, while benefitting laboratory innovators and maximizing system efficiency. Materials and methods A mixed-methods approach including literature review, semi-structured interviews, and a multi-stakeholder forum involving patient representatives (n = 1), laboratory leaders (n = 6), physicians (n = 5), Ministry personnel (n = 4), administrators (n = 3), extra-provincial experts, and researchers (n = 7), as well as pharmaceutical (n = 5) and diagnostic companies (n = 2). The forum considered evidence of good practices in adoption, implementation, and financing laboratory services and identified barriers as well as feasible options for improving advanced diagnostic testing in Ontario. Results Overarching challenges identified included: barriers to define what is needed; need for a clear approach to adoption; and the need for more oversight and coordination. Recommendations to address these included a shift to an anticipatory system of test adoption, creating a fit-for-purpose system of health technology management that consolidates existing evaluation processes, and modernizing the governance and financing of testing so that it is managed at a care-delivery level. Conclusions The proposals for change in Ontario highlight the role that HTA, governance, and financing of health technology play along the continuum of a health technology life cycle within a healthcare system where decision-making is highly decentralized. Resource availability and capacity were not a concern – instead, solutions require higher levels of coordination and system integration along with innovative approaches to HTA.