Application Of SWAP Research Articles

Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries

Rapid technological changes and disruptive innovations have resulted in a significant shift in people’s behavior and requirements. Electronic gadgets, including smartphones, notebooks, and other devices, are indispensable to everyday routines. Consequently, the demand for high-capacity batteries has surged, which has enabled extended device autonomy. An alternative approach to address this demand is battery swapping, which can potentially extend the battery life of electronic devices. Although battery sharing in electric vehicles has been well studied, smartphone applications still need to be explored. Crucially, assessing the batteries’ state of health (SoH) presents a challenge, necessitating consensus on the best estimation methods to develop effective battery swap strategies. This paper proposes a model for estimating the SoH curve of lithium-ion batteries using the state of charge curve. The model was designed for smartphone battery swap applications utilizing Gated Recurrent Unit (GRU) neural networks. To validate the model, a system was developed to conduct destructive tests on batteries and study their behavior over their lifetimes. The results demonstrated the high precision of the model in estimating the SoH of batteries under various charge and discharge parameters. The proposed approach exhibits low computational complexity, low cost, and easily measurable input parameters, making it an attractive solution for smartphone battery swap applications.

Design of a Combined Modular and 3D-Printed Falling Film Solution Layer Crystallizer for Intermediate Purification in Continuous Production of Pharmaceuticals.

A highly scalable combined modular and 3D-printed falling film crystallization device is developed and demonstrated herein; the device uses a small, complex, printed overflow-based film distribution part that ensures formation of a well-distributed heated liquid film around a modular, tubular residence time/crystallizer section, enabling extended residence times to be achieved. A model API (ibuprofen) and impurity (ibuprofen ethyl ester) were used as a test system in the evaluation of the novel crystallizer design. The proposed crystallizer was run using three operational configurations: batch, cyclical batch, and continuous feed, all with intermittent removal of product. Results were suitable for intermediate purification requirements, and stable operation was demonstrated over multiple cycles, indicating that this approach should be compatible with parallel semicontinuous operation for intermediate purification and solvent swap applications in the manufacture of drugs.

Intercropping Simulation Using the SWAP Model: Development of a 2×1D Algorithm

Intercropping is a common cultivation system in sustainable agriculture, allowing crop diversity and better soil surface exploitation. Simulation of intercropped plants with integrated soil–plant–atmosphere models is a challenging procedure due to the requirement of a second spatial dimension for calculating the soil water lateral flux. Evaluations of more straightforward approaches for intercrop modeling are, therefore, mandatory. An adaptation of the 1D model Soil, Water, Atmosphere and Plant coupled to the World Food Studies (SWAP/WOFOST) to simulate intercropping (SWAP 2×1D) based on solar radiation and water partitioning between plant strips was developed and the outcomes are presented. An application of SWAP 2×1D to maize–soybean (MS) strip intercropping was evaluated against the monocropping maize (M) and soybean (S) simulated with the 1D model SWAP/WOFOST, and a sensitivity analysis of SWAP 2×1D was carried out for the intercropping MS. SWAP 2×1D was able to simulate the radiation interception by both crops in the intercropping MS and also to determine the effect of the radiation attenuation by maize on soybean plants. Intercropped plants presented higher transpiration and resulted in lower soil evaporation when compared to their equivalent monocropping cultivation. A numerical issue involving model instability caused by the simulated lateral water flux in the soil from one strip to the other was solved. The most sensitive plant parameters were those related to the taller plant strips in the intercropping, and soil retention curve parameters were overall all significantly sensitive for the water balance simulation. This implementation of the SWAP model presents an opportunity to simulate strip intercropping with a limited number of parameters, including the partitioning of radiation by a well-validated radiation sharing model and of soil water by simulating the lateral soil water fluxes between strips in the 2×1D environment.

Application of the swap model to predict impact of climate change on soil water balance in a Sicilian vineyard

In Sicily, the increasing scarcity of quality water is leading to irrigation with saline water in soils having a considerable susceptibility to salinization and degradation. In this paper, water flow and solute transport in a Sicilian irrigated soil was simulated by using the SWAP model, and the simulated results compared to measured values of soil moisture and salinity. The investigation was carried out in a vineyard located in Sicily (Mazara del Vallo, Trapani Province) within the framework of the Project: Evolution of cropping systems as affected by climate change (CLIMESCO). The soil hydraulic parameters were obtained by inverse method based on multi-step outflow experiments. The results obtained using field measurements from two soil profiles showed that SWAP provided reliable predictions of water content, θ, and of electrical conductivity of saturated extract, ECsat. The effect of climate change on soil water balance was predicted by application of SWAP under the soil-crop and irrigation management conditions currently practiced in the vineyard. Simulations were carried out with climatic data generated by the statistical downscaling process based on the global CGM HADCM3 for two IPCC scenarios (A2 and B1) and for the 2070-2100 time interval. The two mentioned IPCC scenarios (A2 and B1) were then compared to a third reference scenario (BASELINE) obtained by using the climatic data recorded from 1951 to 2004. The results indicated that the only significant effect of climate change was to shorten and to advance the crop cycle length, which decreased of 16 and 27 days respectively in B1 and A2 compared to values obtained for the BASELINE scenario. Further application of SWAP carried out simulating an advance in the irrigation starting time according to the advance in the crop cycle indicated that this could be considered a kind of adaptation strategy to be applied in the future in the vineyard to counterbalance the impact of climate change. This means that in the future farmers might be likely to anticipate the irrigation season in order to avoid any significant reduction in the crop productivity.

Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture

We present an innovative approach to explore water management options in irrigated agriculture considering the constraints of water availability and the heterogeneity of irrigation system properties. The method is two-folds: (i) system characterization using a stochastic data assimilation procedure where the irrigation system properties and operational management practices are estimated using remote sensing (RS) data; and (ii) water management optimization where we explored water management options under various levels of water availability. We set up a soil–water–atmosphere–plant model (SWAP) in a deterministic–stochastic mode for regional modeling. The distributed data, e.g. sowing dates, irrigation practices, soil properties, depth to groundwater and water quality, required as inputs for the regional modeling were estimated by minimizing the residuals between the distributions of field-scale evapotranspiration (ET) simulated by the regional application of SWAP, and by surface energy balance algorithm for land (SEBAL) using two Landsat7 ETM+ images. The derived distributed data were used as inputs in exploring water management options. Genetic algorithm was used in data assimilation and water management optimizations. The case study was conducted in Bata minor (lateral canal), Kaithal, Haryana, India during 2000–2001 rabi (dry) season. Our results showed that under limited water condition, regional wheat yield could improve further if water and crop management practices are considered simultaneously and not independently. Adjusting sowing dates and their distribution in the irrigated area could improve the regional yield, which also complements the practice of deficit irrigation when water availability is largely a constraint. This result was also found in agreement with the scenario that water is non-limited with the exception that the farmers have more degrees of freedom in their agricultural activities. An improvement of the regional yield to 8.5% is expected under the current scenario.