• Energy Research

AbstractThe fluid dynamic behavior of a vibrofluidized bed operating with Geldart C particles was studied. The experiments were conducted in order to observe the influence of amplitude, frequency, and dimensionless vibration number on the minimum fluidization velocity, pressure drop, and standard deviation obtained. It was noted that the dimensionless vibration number should be used very carefully if it is to be the unique parameter to set the vibrational effect of the bed fluid dynamics. The results clearly indicate that the fluid dynamic behavior of the bed is very dependent on the different combinations of amplitude and frequency for the same dimensionless vibration number. Therefore, the use of the amplitude or frequency of vibration and the dimensionless vibration number is recommended for a better characterization of the vibrational effects on the fluid dynamic behavior of the particle bed.

Electronic waste (e-waste) management has become a significant challenge in recent years due to the increasing consumption of electronic devices and their improper disposal. Effective e-waste management requires a comprehensive approach that considers the environmental, economic, and social impacts of e-waste. This comprehensive review provides a critical assessment of e-waste management procedures, encompassing the stages of collection, transportation, treatment, and disposal. Emphasising the significance of embracing sustainable approaches like reusing, repairing, and recycling, the review underscores their pivotal role in mitigating the adverse environmental and human health effects of e-waste. This review provides an overview of e-waste management concerns specifically in India from its collection to the end cycle including toxicological, environmental, and human impacts and a graphical analysis of current and future e-waste trends. It emphasises the need to effectively enforce regulations and establish extended producer responsibility (EPR) to promote sustainable e-waste management practices. Additionally, the review delves into the complexities surrounding e-waste management, such as insufficient infrastructure, resource and funding constraints, and a dearth of awareness among stakeholders. It strongly underscores the necessity for a concerted endeavour involving governments, industries, and communities to tackle these obstacles and advance the cause of efficient e-waste management practices. This paper is valuable to the scientific community as it offers a thorough assessment of e-waste management, focusing on environmental, economic, and social impacts. It emphasises sustainable practices and regulatory measures, providing actionable insights to address e-waste challenges. Overall, this review provides a comprehensive overview of e-waste management and highlights the importance of adopting sustainable practices to address the negative impacts of e-waste on the environment, human health, and the economy.

Abstract Worldwide consumption of energy produced from fossil fuels is forecasted to grow. This trend leads to both environmental pollution and the depletion of fossil fuel resources. Green diesel is a suitable candidate to substitute petroleum based-diesel due to its plentiful raw materials, non-polluting production process, and cost-effectiveness. Green diesel production is seen as simple, efficient, and relatively clean process. Deoxygenation (DO) is crowned as the best available technology to produce green diesel from palm fatty acid distillate (a side product of palm oil production) and other oils using heterogeneous catalysts such as zeolites. The capability of catalysts can be improved by optimizing operating parameters, treating and modifying catalysts and with the use of nano-sized catalytic materials. These activities contribute to stronger and more active Bronsted-Lewis acid-base sites and enlarged crystallite sizes, which improve the DO efficiency, selectivity, and reusability, to produce high-grade green diesel with less oxygen content.

This research was performed to find an alternative, low-cost, competitive, locally available and efficient adsorbent to treat nickel (Ni) containing effluents. For this purpose, several Brazilian agro-wastes like sugarcane bagasse (SCB), passion fruit wastes (PFW), orange peel (OP) and pineapple peel (PP) were compared with an activated carbon (AC). The adsorbents were characterized. Effects of fundamental factors affecting the adsorption were investigated using batch tests. Kinetic and equilibrium studies were performed using conventional models. It was verified that the adsorption was favored at pH of 6.0 for all agro-wastes, being dependent of the Ni speciation, point of zero charge and surface area of the adsorbents. The Ni removal percentage was in the following order: SCB > OP > AC > PFW > PP. From the kinetic viewpoint, the Elovich model was appropriate to fit the Ni adsorption onto SCB, while for the other adsorbents, the pseudo-first-order model was the most suitable. For all adsorbents, the Langmuir model was the more adequate to represent the equilibrium data, being the maximum adsorption capacities of 64.1 mg g−1, 60.7 mg g−1, 63.1 mg g−1, 48.1 mg g−1 and 64.3 mg g−1 for SCB, PFW, OP, PP and AC, respectively. These results indicated that mainly SCB and OP can be used as alternative adsorbents to treat Ni containing effluents.

AbstractThe influence of vibration number Γ, amplitude A, and frequency of vibration f on the fluid dynamic behavior of a vibrofluidized bed operating with inert particles in the transient period of water evaporation and drying of different solutions was evaluated. The solutions investigated were sewage sludge, skimmed milk, and calcium carbonate of three different solids contents. The impact of feed rate and vibration parameters on the dynamic behavior of variables such as pressure drop and inlet air velocity was determined. Based on the results it was verified that the operation at A = 0.015 m improved the bed dynamics and the gas‐particle contact in the wetted beds. As different dynamic behaviors may be obtained under the same Γ value, the values of A or f must be informed together with Γ for a complete characterization of vibration effects.

This study reports experimental data for the liquid–liquid equilibrium (LLE) for a {peanut biodiesel (1) + glycerol (2) + ethanol (3)} system at 303.15 K and 323.15 K at atmospheric pressure. Solubility (binodal) curves were obtained by a cloud-point method. The reliability of the experimental data was confirmed using the Hand and Othmer–Tobias correlations. The regression coefficients were above 0.97 for all data sets. The universal quasichemical model was successfully used to correlate LLE data, which showed root-mean-square deviations of 0.99%, indicating accuracy close to experimental uncertainties.

The purpose of this work is to evaluate the operation of a vibrofluidized bed of inert particles in the transient and steady-state regimes for water evaporation and for drying of domestic sewage sl...

This study aimed to investigate the liquid–liquid equilibrium (LLE) behavior of sesame fatty acid ethyl ester (FAEE) and methyl ester (FAME) in combination with glycerol and the co-solvents ethanol and methanol. FAEE and FAME were produced through the transesterification of mechanically extracted and purified sesame oil, using potassium hydroxide (KOH) as a homogeneous base catalyst. The reactions were conducted in ethanol and methanol to produce FAEE and FAME, respectively. Post-reaction, the products were separated and purified, followed by an analysis of the LLE behavior at 313.15 K and 323.15 K under atmospheric pressure (101.3 kPa). The experimental process for the miscibility analysis utilized a jacketed glass cell adapted for this study. Miscibility limits or binodal curves were determined using the turbidity-point method. Tie lines were constructed by preparing mixtures of known concentrations within the two-phase region, which allowed the phases to separate after agitation. Samples from both phases were analyzed to determine their composition. This study revealed that higher temperatures promoted greater phase separation and enhanced the biodiesel purification process. The NRTL model effectively correlated the activity coefficients with the experimental data, showing good agreement, with a root-mean-square deviation of 3.5%. Additionally, the data quality was validated using Marcilla’s method, which yielded an R2 value close to 1. Attraction factors and distribution coefficients were also calculated to evaluate the efficiency of the co-solvents as extraction agents. The findings indicated higher selectivity for methanol than for ethanol, with varying degrees of distribution among the co-solvents. These results offer significant insights into enhancing biodiesel production processes by considering the effects of co-solvents on the LLE properties of mixtures, ultimately contributing to more efficient and cost-effective biodiesel production.

Abstract Drying of inorganic particulate compounds is recognized as an energy-intensive process. Due to this fact, optimization studies through modeling and simulation of experimental data play a critical role towards process economic benefits. The main drawback of some models from literature is their dependence on operating parameters and dryer type, which can be a key issue for data generalization. Artificial neural networks may provide the first step to solve this problem. This study analyses the feasibility of neural models to fit and estimate drying kinetics data, cumulative and instantaneous energy efficiency indices from fixed, fluidized and vibro-fluidized bed dryers at different operating conditions. The networks were trained considering different scenarios for both drying kinetics and energy analysis. It was shown that the neural model is consistent to estimate new patterns not addressed in the trainings for the case in which the database is regarded for a single type of dryer. Simultaneous training considering multiple datasets of each dryer resulted in predictions with poor accuracy, but considering the complex hydrodynamic conditions of the moving beds, there is room for improvement when efficient data is used. In this way, neural models can be considered an interesting tool to predict parameters also for energy analysis.