Characteristics of sunflower seed drying and microwave energy consumption

The effect of the microwave-convective drying technique on the moisture ratio, drying rate, drying time, effective moisture diffusivity, microwave specific energy consumption, and energy efficiency of sunflower seedswere investigated.Drying took place in the falling rate period. Increasing the microwave power caused a significant decrease in the drying time. The drying data were fitted to four thin-layer drying models. The performance of these models was compared using the coefficient of determination, reduced chi-square and root mean square error between the observed and predicted moisture ratios. The results showed that the Page model was found to satisfactorily describe themicrowave-convective drying curves of sunflower seeds. The effective moisture diffusivity values were estimated from Fick diffusion model and varied from 1.73 10-7 to 4.76 10-7m2s-1. Increasing the microwave power resulted in a considerable increase in drying efficiency and a significant decrease in microwave specific energy consumption. The highest energy efficiency and the lowestmicrowave specific energy consumption were obtained at the microwave power of 300 W

Characteristics of sunflower seed drying and microwave energy consumption

Abstract The effect of the microwave-convective drying technique on the moisture ratio, drying rate, drying time, effective moisture diffusivity, microwave specific energy consumption, and energy efficiency of sunflower seedswere investigated.Drying took place in the falling rate period. Increasing the microwave power caused a significant decrease in the drying time. The drying data were fitted to four thin-layer drying models. The performance of these models was compared using the coefficient of determination, reduced chi-square and root mean square error between the observed and predicted moisture ratios. The results showed that the Page model was found to satisfactorily describe themicrowave-convective drying curves of sunflower seeds. The effective moisture diffusivity values were estimated from Fick diffusion model and varied from 1.73 10-7 to 4.76 10-7m2s-1. Increasing the microwave power resulted in a considerable increase in drying efficiency and a significant decrease in microwave specific energy consumption. The highest energy efficiency and the lowestmicrowave specific energy consumption were obtained at the microwave power of 300 W.</jats:p

On Structured Design Space Exploration for Mapping of Quantum Algorithms

Quantum algorithms can be expressed as quantum circuits when the circuit model of computation is adopted. Such a circuit description is usually hardware-agnostic, that is, it does not consider the limitations that the quantum hardware might have. In order to make quantum algorithms executable on quantum devices they need to comply to their constraints, which mainly affect the parallelism of quantum operations and the possible interactions between the qubits. The process of adapting a quantum circuit to meet the quantum chip restrictions is known as mapping. The resulting circuit usually has a higher number of gates and depth, decreasing the algorithm's reliability. Different mapping solutions have been already proposed. Most of them are meant for a specific quantum processor and differ in methodology, approach and features. In addition, they are usually only compared in terms of added gates, circuit depth and compilation time. No thorough comparative analysis of the different mapping solutions performance and features has been performed so far.In this paper, we propose to apply structured design space exploration (DSE) methodologies to the mapping procedures. This will allow not only to have a more in depth and structured analysis of their performance but also to identify what features are key and worth to implement. By using DSE we will be able to: i) determine in what regimes some mapping solutions outperform others; ii) derive optimal mapping strategies for specific quantum algorithms and quantum processors; and iii) perform an scalability analysis. In addition, DSE techniques cannot only be applied to the mapping layer that is key for bridging quantum applications to quantum devices, but also to the full-stack quantum computing system allowing for its crosslayer co-design.QCD/Almudever La

On Structured Design Space Exploration for Mapping of Quantum Algorithms

Quantum algorithms can be expressed as quantum circuits when the circuit model of computation is adopted. Such a circuit description is usually hardware-agnostic, that is, it does not consider the limitations that the quantum hardware might have. In order to make quantum algorithms executable on quantum devices they need to comply to their constraints, which mainly affect the parallelism of quantum operations and the possible interactions between the qubits. The process of adapting a quantum circuit to meet the quantum chip restrictions is known as mapping. The resulting circuit usually has a higher number of gates and depth, decreasing the algorithm's reliability. Different mapping solutions have been already proposed. Most of them are meant for a specific quantum processor and differ in methodology, approach and features. In addition, they are usually only compared in terms of added gates, circuit depth and compilation time. No thorough comparative analysis of the different mapping solutions performance and features has been performed so far.In this paper, we propose to apply structured design space exploration (DSE) methodologies to the mapping procedures. This will allow not only to have a more in depth and structured analysis of their performance but also to identify what features are key and worth to implement. By using DSE we will be able to: i) determine in what regimes some mapping solutions outperform others; ii) derive optimal mapping strategies for specific quantum algorithms and quantum processors; and iii) perform an scalability analysis. In addition, DSE techniques cannot only be applied to the mapping layer that is key for bridging quantum applications to quantum devices, but also to the full-stack quantum computing system allowing for its crosslayer co-design.</p