We present a novel hybrid membrane system that operates as a heat engine capable of utilizing low-grade thermal energy, which is not readily recoverable with existing technologies. The closed-loop system combines membrane distillation (MD), which generates concentrated and pure water streams by thermal separation, and pressure retarded osmosis (PRO), which converts the energy of mixing to electricity by a hydro-turbine. The PRO-MD system was modeled by coupling the mass and energy flows between the thermal separation (MD) and power generation (PRO) stages for heat source temperatures ranging from 40 to 80 °C and working concentrations of 1.0, 2.0, and 4.0 mol/kg NaCl. The factors controlling the energy efficiency of the heat engine were evaluated for both limited and unlimited mass and heat transfer kinetics in the thermal separation stage. In both cases, the relative flow rate between the MD permeate (distillate) and feed streams is identified as an important operation parameter. There is an optimal relative flow rate that maximizes the overall energy efficiency of the PRO-MD system for given working temperatures and concentration. In the case of unlimited mass and heat transfer kinetics, the energy efficiency of the system can be analytically determined based on thermodynamics. Our assessment indicates that the hybrid PRO-MD system can theoretically achieve an energy efficiency of 9.8% (81.6% of the Carnot efficiency) with hot and cold working temperatures of 60 and 20 °C, respectively, and a working solution of 1.0 M NaCl. When mass and heat transfer kinetics are limited, conditions that more closely represent actual operations, the practical energy efficiency will be lower than the theoretically achievable efficiency. In such practical operations, utilizing a higher working concentration will yield greater energy efficiency. Overall, our study demonstrates the theoretical viability of the PRO-MD system and identifies the key factors for performance optimization.
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Instituto de Ingeniería Energética, Universitat Politècnica de València, Valencia, Spain.
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Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of a chiral edge state and a topological surface state.
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