Band 20, Heft 4

The escalating global concern over climate change and the urgent need for sustainable solutions have propelled renewable energy technologies into the spotlight. Among these, renewable refrigeration cycles have emerged as a promising research area, offering environmentally friendly alternatives to traditional refrigeration systems. Ejector refrigeration cycles (ERCs), particularly those employing water as the working fluid, have garnered significant attention due to their inherent advantages. To further optimize the performance of ERCs, a comprehensive understanding of the underlying flow phenomena and their impact on system efficiency is crucial. The primary objective is to explore the impact of secondary flow superheating on the ejector’s performance and flow characteristics. A parametric study is conducted by varying the secondary flow superheating degree from 0 K to 30 K while maintaining constant secondary flow pressures of 1,200 Pa and 1,800 Pa. The results revealed that while the overall flow pattern remained relatively unaffected by the increase in superheating, the ejector’s performance and exergy destruction were significantly impacted. A noteworthy observation is the inverse relationship between secondary flow superheating and entrainment ratio. As the superheating degree increases, the entrainment ratio exhibits a corresponding decrease. For instance, a 30-degree increase in superheat resulted in a 2.8 % and 3.9 % reduction in entrainment ratio at 1,200 Pa and 1,800 Pa, respectively.

Reducing pressure drop and energy requirements in the natural gas transmission is crucial for enhancing the performance of pipelines and reducing the greenhouse emission effect. In this study, a steady-state CFD modeling was conducted to examine the pressure drop, energy-specific toll (EST), and entropy generation rate (ER) of natural gas pipelines in the dense phase (DP), pseudo-dense phase (PDP), and vapor phase (VP). Three natural gas cases with varying compositions were utilized. The findings revealed that at all three cases, the pressure drop, EST, and ER in the DP were lower compared to those in the PDP and VP phases. For instance, in case 1, the EST in DP was 15 % and 67 % lower than in the PDP and VP, respectively. Similarly, the pressure drop in DP for case 1 was 7.5 % and 42.8 % lower than in the PDP and VP, respectively. Additionally, the ER in DP for case 1 was 12 % and 60 % lower than in the PDP and VP, respectively. The effect of mass flow rate on the pipeline performance indicated that as the mass flow rate increased from 30 to 50 kg/s, the pressure drop, EST, and ER for all cases and phases increased almost 2.7 times. Additionally, when the pipeline diameter increased from 0.3 to 0.6 m, the pressure drop, EST, and ER decreased almost 38, 38, and 16 times, respectively. The results of surface roughness revealed that for all cases and phases, the pressure drop, EST, and ER increased by almost 2.39, 2.39, and 2.2 times, respectively, as the surface roughness increased from 5 μm to 260 μm. Finally, this study developed mathematical models to investigate the pressure drop for pipelines in DP and PDP. The diameter of the pipeline had a greater effect on presser drop compared to the inlet mass flow rate and surface roughness.

Energy is vital for life and human development, with global warming due to activities such as the combustion of fossil fuels and deforestation emitting dangerous greenhouse gases, changing the climate of the Earth. Global energy demand is increasing, with developed nations viewing buildings as major energy consumers. Due to the long lifespan of buildings, it is important to evaluate their suitability to future climate change and possible changes in energy consumption. Appraisal of the cooling loads in each building is now required due to rising energy costs and the need to reduce the impacts of climate change caused by energy consumption from fossil fuels in buildings. This paper aims to apply Random Forest Regression (RF) and Support Vector Regression (SVR), well-known machine learning algorithms to predict building cooling loads. It utilizes the Jellyfish Search Optimizer (JSO) and Transit Search Optimization Algorithm (TSOA) to enhance accuracy and minimize overall error in Cooling Load (CL) estimation. The investigation suggests two high-performance schemes, applies two optimizers for hybrid schemes, and utilizes an ensemble approach for accurate appraisal . Moreover, the SHAP method is utilized to compare the effectiveness of the parameters. The research proves to be insightful in constructing CL projection and suggests that a RFJS-based model is the most effective way to optimize energy consumption. The hybrid model attained an R 2 of 0.994 at its best and RMSE of 0.744. Other than this, the following effective ensemble approach was RSJS, whose R 2 and RMSE were 0.989 and 0.985, accordingly. The third best-performing model was SVJS with R 2 and RMSE values of 0.972 and 1.583, accordingly.

The gas distribution system is a network designed to deliver natural gas (NG) from production to end users. A very high pressure is maintained at the production site to ensure efficient transportation through the pipeline system. It must then be depressurized at the point of consumption, typically through several stages. A significant amount of energy is wasted during the depressurization of NG at city gas stations (CGS), where throttling is employed. The pressure in the gas distribution system at many depressurization gates ranges from 400 to 700 psi. The problem is that a steep decline in temperature occurs while throttling resulting in NG hydrate formation due to the presence of water vapor in the stream. NG hydrate has been of importance for energy storage. However, it may cause disasters in gas pipelines during throttling in CGS. Therefore, removing the hydrate structure is vital to prevent the stream from blockage, for which large heaters are being used. The available kinetic energy within the flowing stream can be a source of heat to preclude hydrate formation. In this article, a simulation study is presented according to Birjand CGS measured data. The results show that during winter, almost 90 percent of the required energy for preheating can be produced using the NG high-pressure mechanical energy.

This investigation presents nucleate pool boiling of water using an experimental setup as well as through numerical simulation. The experimental setup consists of a 10 × 8 square inch boiling vessel and a 5-inch horizontal copper heating plate with cartridge electrical heater. Further, 2D numerical simulation on similar geometry was also performed using RPI (Rensselaer Polytechnic Institute) boiling model in commercial simulation software FLUENT Release 16.2 (of Fluent Inc., USA). The results were validated with the experimental data of this investigation as well as with the data available in various literature. Thereafter, the simulation model for the horizontal surface was extended to study the effects of variation in the angle of inclination of the base heater plate from 0° to 45° in intervals of 5°, keeping the heating surface towards the boiling liquid. Further, the effects of heating surface roughness were also studied. On comparing the data with the horizontal surface, the heat transfer was found to be dependent upon the angle of inclination of heating surface and an increasing trend in heat transfer coefficient was observed, while the onset of bubble formation for all the angles was observed at the lowermost position of the plate, heading upwards. The computational study of roughness over heater plate was also cogent enough to conclude enhancement in heat transfer over treated surfaces.

The cost-effective and qualified weld is a primary requirement in fabrication of SS316 structural applications. Thin sheets of this material, characterized by a very thin invisible anti corrosive oxide film of Chromium and Molybdenum on the surface, require low heat input and optimized process parameters to avoid burn through during welding and to achieve the qualified weld. These facts have made it challenging to produce a qualified weld joint of this material by plasma arc welding. In this study process parameters for single pass plasma arc welding of austenitic stainless steel 316 are optimized by using response surface methodology (RSM). A key-hole mode plasma arc welding (PAW) is used to weld the square butt 2 mm thick plates of SS 316 without filler wire to reduce the cost. A regular two-level trail experiments designed by Design-Expert® software were conducted to establish lower and upper limit values of the input variables and their output responses. The welding current of 100 Amp, voltage of 24 V, speed of 300 mm/min and back shielding gas flow rate of 20 L/min are determined to be optimized values of process parameters, which achieved the adequate values of responses; front bead height, front bead width, penetration height and back penetration width ensuring the qualified weld. Moreover, an empirical mathematical model for predicting responses is constructed and the coefficients are determined using regression analysis. The adequacy of developed model is verified by analysis of variance (ANOVA) and is validated against experimental results.

Gasification is a highly potential technology in the energy sector, which involves complex chemical and high-temperature thermal processes. It is primarily a process that alters biomass into a compound of valuable fuel gases and byproducts through a series of chemical reactions like oxidation, reduction, and pyrolysis. The gaseous products so produced are important constituents in energy generation. Solid residues like char and ash are also produced as byproducts. Despite its potential, biomass gasification is a highly complicated process and requires extensive modeling and simulation studies. The Radial Basis Function (RBF) model, which is highly recognized for tracking complex links using historical data, is one of them. On top of that, for further fine-tuning of the projection accuracy, advanced enhancement schemes such as Flying Foxes Optimization (FFO) and Hunger Game Search Optimization (HGSO) are used in this work. From this point of view, the developed model presents substantial advances concerning the optimization of the gasification process, taking into account the large emphasis on sustainability and major untapped opportunities that data-driven approaches create within this key field. A comparative evaluation based on R 2 and RMSE values demonstrates that the RBF coupled with the FFO (RBFF) model significantly outperforms both the conventional RBF and the RBF coupled with HGSO (RBHG) schemes. For CH 4 projection, RBFF achieves an R 2 of 0.991 with an RMSE of 0.314, surpassing RBF ( R 2  = 0.973, RMSE = 0.543) and RBHG ( R 2  = 0.983, RMSE = 0.431). Similarly, for C 2 H n , RBFF attains the peak R 2 of 0.991 and the lowest RMSE of 0.135, compared to RBF ( R 2  = 0.973, RMSE = 0.229) and RBHG ( R 2  = 0.981, RMSE = 0.196). These findings stress the impact of hybrid enhancement in adjusting gasification modeling, reinforcing the RBFF model’s effectiveness in advancing gasification technology.

Fossil fuel dependence causes environmental and resource issues, intensified by climate change and population growth. Renewable sources like solar, wind, and biomass are rising. Biomass contributes 10–14 % of global energy, with gasification offering stable output and useful byproducts. However, efficiency and environmental concerns challenge its economic viability, prompting the need for predictive models under diverse conditions. This study introduces a novel hybrid modeling approach that integrates Naive Bayes (NB) with two advanced metaheuristic optimization algorithms, Jellyfish Search Optimizer (JSO) and Cheetah Optimizer (CO), to enhance the prediction accuracy of elemental compositions of nitrogen and hydrogen from proximate biomass data. The proposed hybrid schemes indicate drastically improved estimation accuracy, and among these, NBCO, i.e., the Naive Bayes + Cheetah Optimizer hybrid, was the most effective. NBCO achieved the RMSE values of 1.472 and 1.955 for nitrogen and hydrogen, validating its better prediction ability. By utilizing the synergistic properties of NB with JSO and CO, this work presents a sound predictive model complementing biomass gasification modeling with a viable tool for optimizing renewable energy processes. With the enhanced accuracy of prediction of elemental composition, the proposed schemes enable better control of biomass gasification processes with increased efficiency in energy production, reduced emissions, and decreased operation costs. Accuracy in determining nitrogen and hydrogen compositions optimizes gasifier efficiency, enabling cleaner, more cost-effective energy production. The model provides a feasible solution for businesses and policymakers seeking to maximize the potential of biomass as a renewable energy source while minimizing environmental and economic problems.