A Review on Physical Hydrogen Storage: Insights into Influencing Parameters, Energy Consumption, Global Outlook and Bibliographic Analysis.

The transition of the global energy market towards an environment-friendly, sustainable society requires a profound transformation from fossil fuel to zero carbon emission fuel. To cope with this goal production of renewable energy is accelerating worldwide. Hydrogen is a clean energy carrier, due to its clean combustion and abundance. Nonetheless, its storage is a critical challenge to its success. Hydrogen must be stored long after being produced and transported to a storage site. Physical hydrogen storage (PHS) is vital among hydrogen storage modes, and its shortcoming needs to overcome for its successful and economic benefits. This review intends to discuss the techniques and applications of physical hydrogen storage in the state of compressed gas, liquefied hydrogen gas, and cold/cryo compressed gas concerning their working principle, chemical and physical properties, influencing factors for physical hydrogen storage, and transportation, economics, and global outlook. In addition, insights of several probable PHS systems are highlighted. The outcomes of this review envisioned that the PHS still necessitates technological advancements despite having remarkable success. The limitation opens the door to further research, which would be helpful for efficient and long-term physical hydrogen storage.

Enhancing drilling mud performance through CMITS-modified formulations: rheological insights and performance optimization.

In the context of deep well drilling, the addition of functionalized additives into mud systems becomes imperative due to the adverse impact of elevated borehole temperatures and salts on conventional additives, causing them to compromise their intrinsic functionalities. Numerous biomaterials have undergone modifications and have been evaluated in drilling muds. However, the addition of dually modified tapioca starch in bentonite-free mud systems remains a notable gap within the existing literature. This study aims to examine the performance of dually modified carboxymethyl irradiated tapioca starch (CMITS) under high temperature and salt-containing conditions employing central composite design approach; the study evaluates the modified starch's impact on mud rheology, thermal stability, and salt resistance. The findings indicated that higher DS (0.66) and CMITS concentrations (8 ppb) improved plastic viscosity (PV), yield point (YP) and gel strength (GS), while increased salt and temperature decreased it, demonstrating the complex interplay of these factors on mud rheology. The developed empirical models suggested that DS 0.66 starch addition enhanced rheology, especially at elevated temperatures, demonstrating improved borehole cleaning potential, supported by quadratic model performance indicators in line with American Petroleum Institute (API) ranges. The optimized samples showed a non-Newtonian behavior, and Power-law model fitting yields promising results for improved cuttings transportation with starch additives.

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data.

In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others.

Silver Conductive Threads-Based Embroidered Electrodes on Textiles as Moisture Sensors for Fluid Detection in Biomedical Applications.

Wearable sensors have become part of our daily life for health monitoring. The detection of moisture content is critical for many applications. In the present research, textile-based embroidered sensors were developed that can be integrated with a bandage for wound management purposes. The sensor comprised an interdigitated electrode embroidered on a cotton substrate with silver-tech 150 and HC 12 threads, respectively, that have silver coated continuous filaments and 100% polyamide with silver-plated yarn. The said sensor is a capacitive sensor with some leakage. The change in the dielectric constant of the substrate as a result of moisture affects the value of capacitance and, thus, the admittance of the sensor. The moisture sensor's operation is verified by measuring its admittance at 1 MHz and the change in moisture level (1-50) µL. It is observed that the sensitivity of both sensors is comparable. The identically fabricated sensors show similar response and sensitivity while wash test shows the stability of sensor after washing. The developed sensor is also able to detect the moisture caused by both artificial sweat and blood serum, which will be of value in developing new sensors tomorrow for smart wound-dressing applications.