Investigation of Performance Enhancements for a Liquid-Desiccant Solar Air-Conditioner

Thermally driven liquid-desiccant air-conditioners (LDAC) are a proven but still developing technology. LDACs can use a solar thermal system to reduce the operational cost and environmental impact of the system by reducing the amount of fuel (e.g. natural gas, propane, etc.) used to drive the system. LDACs also have a key benefit of being able to store energy in the form of concentrated desiccant storage. TRNSYS simulations were used to evaluate several different methods of improving the thermal and electrical coefficients of performance (COPt and COPe) and the solar fraction (SF) of a LDAC. The study analyzed a typical June to August cooling season in Toronto, Ontario. Utilizing properly sized, high-efficiency pumps increased the COPe to 3.67, an improvement of 55%. A new design, featuring a heat recovery ventilator on the scavenging-airstream and an energy recovery ventilator on the process-airstream, increased the COPt to 0.58, an improvement of 32%. This also improved the SF slightly to 54%, an increase of 8%. A new TRNSYS TYPE was created to model a stratified desiccant storage tank. Different volumes of desiccant were tested with a range of solar array system sizes. The largest storage tank coupled with the largest solar thermal array showed improvements of 64% in SF, increasing the value to 82%. The COPe was also improved by 17% and the COPt by 9%. When combining the heat recovery systems and the desiccant storage systems, the simulation results showed a 78% increase in COPe and 30% increase in COPt. A 77% improvement in SF and a 17% increase in total cooling rate were also predicted by the simulation. The total thermal energy consumed was 10% lower and the electrical consumption was 34% lower. The amount of non-renewable energy needed from the natural gas boiler was 77% lower. Comparisons were also made between LDACs and vapour-compression (VC) systems. Dependent on set-up, LDACs provided higher latent cooling rates and reduced electrical power consumption. Negatively, a thermal input was required for the LDAC systems but not for the VC systems.

Communication-efficient decentralized sequential detection

The problem of decentralized sequential detection is studied in this thesis, where local sensors are memoryless, receive independent observations, and no feedback from the fusion center. In addition to traditional criteria of detection delay and error probability, we introduce a new constraint: the number of communications between local sensors and the fusion center. This metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. A new formulation for communication-efficient decentralized sequential detection is proposed where the overall detection delay is minimized with constraints on both error probabilities and the communication cost. Two types of problems are investigated based on the communication-efficient formulation: decentralized hypothesis testing and decentralized change detection. In the former case, an asymptotically person-by-person optimum detection framework is developed, where the fusion center performs a sequential probability ratio test based on dependent observations. The proposed algorithm utilizes not only reported statistics from local sensors, but also the reporting times. The asymptotically relative efficiency of proposed algorithm with respect to the centralized strategy is expressed in closed form. When the probabilities of false alarm and missed detection are close to one another, a reduced-complexity algorithm is proposed based on a Poisson arrival approximation. In addition, decentralized change detection with a communication cost constraint is also investigated. A person-by-person optimum change detection algorithm is proposed, where transmissions of sensing reports are modeled as a Poisson process. The optimum threshold value is obtained through dynamic programming. An alternative method with a simpler fusion rule is also proposed, where the threshold values in the algorithm are determined by a combination of sequential detection analysis and constrained optimization. In both decentralized hypothesis testing and change detection problems, tradeoffs in parameter choices are investigated through Monte Carlo simulations.