Search results for "Performance prediction"
showing 3 items of 13 documents
Bipolar membrane reverse electrodialysis for the sustainable recovery of energy from pH gradients of industrial wastewater: Performance prediction by…
2021
Abstract The theoretical energy density extractable from acidic and alkaline solutions is higher than 20 kWh m−3 of single solution when mixing 1 M concentrated streams. Therefore, acidic and alkaline industrial wastewater have a huge potential for the recovery of energy. To this purpose, bipolar membrane reverse electrodialysis (BMRED) is an interesting, yet poorly studied technology for the conversion of the mixing entropy of solutions at different pH into electricity. Although it shows promising performance, only few works have been presented in the literature so far, and no comprehensive models have been developed yet. This work presents a mathematical multi-scale model based on a semi-…
Multi-physical modelling of reverse electrodialysis
2017
Abstract Reverse electrodialysis (RED) is an electrochemical membrane process that directly converts the energy associated with the concentration difference between two salt solutions into electrical energy by means of a selective controlled mixing. The physics of RED involves the interaction of several phenomena of different nature and space-time scales. Therefore, mathematical modelling and numerical simulation tools are crucial for performance prediction. In this work, a multi-physical modelling approach for the simulation of RED units was developed. A periodic portion of a single cell pair was simulated in two dimensions. Fluid dynamics was simulated by the Navier-Stokes and continuity …
Kinetic analysis of functional images: The case for a practical approach to performance prediction
1999
We present the first parallel medical application for the analysis of dynamic positron emission tomography (PET) images together with a practical performance model. The parallel application may improve the diagnosis for a patient (e. g. in epilepsy surgery) because it enables the fast computation of parametric images on a pixed level as opposed to the traditionally used region of interest (ROI) approach which is applied to determine an average parametric value for a particular anatomic region of the brain. We derive the performance model from the application context and show its relation to abstract machine models. We demonstrate the accuracy of the model to predict the runtime of the appli…