Normalized Exergy Destruction Research Articles

Exergy analysis of a biorefinery process for co-production of third-generation L-lactic acid and electricity from Eucheuma denticulatum residues

This paper evaluates the thermodynamic performance of three different l-lactic acid (LLA) biorefinery scenarios, utilizing macroalgae cellulosic residue, namely Eucheuma denticulatum residues (EDRs), as raw materials. To establish exergy analysis, the thermodynamic properties of each biorefinery scenario was obtained through process simulation using Aspen Plus® V10. It was found that microwave-assisted autohydrolysis (MAA) was feasible in minimizing the amount of EDRs needed by 70.2% to produce 1000 kg/h of LLA. The fermentation unit, wastewater treatment (WWT) unit, and combined heat and power (CHP) unit were ranked top three for the occurrence of exergy destruction. Both the LLA recovery unit (99.37%) and MAA pretreatment unit (99.68%) were found to be highly exergetically efficient. Among the three scenarios, Scenario III, which was integrated with power generation and a proper waste management system, had the highest functional exergy efficiency (22.12%) and normalized exergy destruction (0.73). It demonstrated the highest functionality due to the prevention of material losses by converting waste streams into value-added products and reducing reliance on fossil-fuel derived energy for plant operation. The valuable information from exergy analysis provides theoretical guidance for performance enhancement in a macroalgae-based biorefinery to produce LLA, either with process integration or technology advancement.

Multi-response optimization of transesterification reaction for biodiesel production from castor oil assisted by hydrodynamic cavitation

With the increasing energy demand, fossil fuel depletion rate is increasing, therefore a need to develop sustainable and energy-efficient biodiesel production systems is arising which can compensate for the rate of fossil fuel depletion. The current study aims to analyze biodiesel production from castor oil using a novel hydrodynamic cavitation reactor. Response surface methodology (RSM) coupled with Box-Behnken design was incorporated to carry out the optimization of reaction parameters (reaction temperature, methanol to oil molar ratio, catalyst amount, and reaction time) to obtain desired castor oil methyl ester (COME) yield, functional exergy efficiency (FEE), Universal exergy efficiency (UEE) and normalized exergy destruction (NED). The developed model shows higher goodness of fit with values of R2 > 99% and R2adj > 98% for all these responses. Desirable reaction conditions for maximum COME yield (92.31 %) were: 57.9 °C temperature, 10.3:1 MeOH:Oil molar ratio, 1.05 %wt. catalyst amount and 58.94 min reaction time whereas based on exergetic indicators along with COME yield using multi-response optimization the same were: 60.3 °C temperature, 9.82:1 MeOH:Oil molar ratio, 1.06 %wt. catalyst amount and 50.86 min reaction time. The COME yield of 93.83 % and 92.27 ± 0.43%, FEE of 86.85% and 85.13 ± 0.40%, UEE of 98.21% and 98.22 ± 0.18%, NED of 0.14 and 0.13 ± 0.01 were predicted and experimentally calculated respectively at optimized reaction conditions considering composite desirability of all the responses. Consideration of exergetic indicators in multi-response optimization using RSM resulted in reduction of reaction time and exergy destruction by 14% and 15% respectively.

Multi-objective exergetic and technical optimization of a piezoelectric ultrasonic reactor applied to synthesize biodiesel from waste cooking oil (WCO) using soft computing techniques

This work was devoted to optimizing the operating conditions of a low-energy consumption, high frequency piezoelectric ultrasonic reactor used for biodiesel production from waste cooking oil (WCO). The optimization was conducted on the basis of exergetic and technical constraints using a consolidated combination of soft computing techniques. Transesterification temperature, residence time, methanol/oil molar ratio were input variables, while functional exergy efficiency (FEE), universal exergy efficiency (UEE), normalized exergy destruction (NED), and conversion efficiency (CE) were considered as output parameters. Four independent ANFIS models were first developed to map the outputs as a function of the inputs. The developed ANFIS models were then introduced to a hybridized optimization paradigm obtained by consolidating the elitist non-dominated sorting genetic algorithm (NSGA-II) and linear interdependent fuzzy multi-objective optimization (ALIFMO) approaches. The optimization was set to maximize the FEE and UEE and to minimize the NED, while satisfying the ASTM standard on CE (i.e., biodiesel content >96.5%). All the developed ANFIS models excellently estimated the outputs with an R2 ≈ 1.0. The transesterification temperature of 60 °C, residence time of 10 min, and methanol/oil molar ratio of 6.20 were selected as the best operating conditions among the spectrum of the solutions suggested by the developed approach.

On the exergetic optimization of solketalacetin synthesis as a green fuel additive through ketalization of glycerol-derived monoacetin with acetone

Abstract This study was devoted to an exergetically investigation and optimization of the operating conditions of an easy-to-scale-up continuous reactor applied for solketalacetin synthesis as a green fuel additive from glycerol-derived monoacetin in the presence of Purolite PD 206 catalyst. The process consisted of two steps, i.e., monoacetin synthesis by glycerol esterification with acetic acid followed by solketalacetin synthesis through reaction of the produced monoacetin with acetone. The main goal of this research was to assess the effects of reaction temperature (20–80 °C), acetone to monoacetin molar ratio (1–5), catalyst loading (0.5–2.5 g), feed flow rate (0.2–1 mL/min), and pressure (0–120 bar) on the exergetic performance parameters of the second stage of the process. Response surface methodology (RSM) was also used to optimize the operating conditions of the reactor by maximizing functional exergetic efficiency (FEE) and minimizing normalized exergy destruction (NED), simultaneously. Overall, feed flow rate had the highest impact on the exergetic performance parameters of the reactor while these indicators were not significantly influenced by pressure. RSM successfully modeled both exergetic parameters with an R2 higher than 0.99. Reaction temperature of 30.8 °C, acetone to monoacetin molar ratio of 2.7, catalyst loading of 1.6 g, feed flow rate of 1.0 mL/min, and pressure of 14.5 bar yielding FEE of 20.39% and NED of 0.90 were determined as the best operating conditions of the reactor. According to the results archived, process yield alone could not stand as the primary objective for making decisions on the optimal operating conditions of the chemical reactors, further highlighting the significance of taking energetic parameters into account in parallel.

Exergy-based optimization of a continuous reactor applied to produce value-added chemicals from glycerol through esterification with acetic acid

The aim of this study was to develop a thermodynamic model for a better understanding of glycerol esterification process with acetic acid in a continuous reactor. Response surface method (RSM) was also used to optimize the operating conditions of the reactor applied for synthesizing acetins from the exergetic viewpoint. The effects of reaction temperature (100–140 °C), feed flow rate (0.2–0.6 mL/min), acetic acid to glycerol molar ratio (1–3), and pressure (1–160 bar) on the exergetic performance parameters of the process were studied. The optimum operating conditions of the reactor were determined by maximizing universal and functional exergetic efficiencies and minimizing normalized exergy destruction, simultaneously. Overall, RSM predicted the exergetic performance parameters of the reactor with an R2 higher than 0.98. Reaction temperature of 117 °C, feed flow rate of 0.6 mL/min, acetic acid to glycerol molar ratio of 1.1, and pressure of 1 bar yielding normalized exergy destruction of 3.1, universal exergetic efficiency of 37.5%, and functional exergetic efficiency of 20.8%, were found as the best operating conditions of the reactor. The results showed that, unlike exergy analysis, process yield (glycerol conversion) alone could not be a perfect performance metric in order to make decisions on the operating conditions of the reactor. The optimum operating conditions found in the current study could be applied at commercial scale for resource-efficient, cost-effective, and eco-friendly valorization of glycerol into acetins.

Multi-objective exergy-based optimization of continuous glycerol ketalization to synthesize solketal as a biodiesel additive in subcritical acetone

This study was aimed at exergetically investigating and optimizing a continuous reactor applied to valorize glycerol into solketal as a biodiesel additive with subcritical acetone in the presence of Purolite PD206. The effects of reaction temperature (20–100 °C), acetone to glycerol molar ratio (1–5), feed flow rate (0.1–0.5 mL/min), pressure (1–120 bar), and catalyst mass (0.5–2.5 g) were evaluated on the exergetic performance parameters of the reactor. In order to optimize the operating conditions of the reactor, adaptive neuro-fuzzy inference system (ANFIS) was coupled with non-dominated sorting genetic algorithm-II (NSGA-II). The ANFIS was applied to develop objective functions on the basis of the process parameters. The developed objective functions were then fed into the NSGA-II to find the optimum operating conditions of the process by simultaneously maximizing universal and functional exergetic efficiencies and minimizing normalized exergy destruction. Overall, the process parameters significantly affected the exergetic performance of the reactor. The ANFIS approach successfully modeled the objective functions with a correlation coefficient higher than 0.99. The optimal ketalization conditions of glycerol were: reaction temperature = 40.66 °C, acetone to glycerol molar ratio = 4.97, feed flow rate = 0.49 mL/min, pressure = 42.31 bar, and catalyst mass = 0.50 g. These conditions could be applied in pilot- or industrial-scale reactors for converting glycerol into value-added solketal in a resource-efficient, cost-effective, and environmentally-friendly manner.

Exergy analysis of a lignocellulosic-based biorefinery annexed to a sugarcane mill for simultaneous lactic acid and electricity production

This study was aimed at exergetically analyzing a lignocellulosic biorefinery annexed to a sugar mill for simultaneous lactic acid and electricity production using sugarcane bagasse and brown leaves as feedstock. The main goal was to reveal the reasons and sources of thermodynamic inefficiencies associated with the biorefinery under investigation. After presenting the exergy balance equations for all the subunits of the biorefinery, their exergetic performance parameters were computed and consequently exergetic performance parameters were measured for the whole system. The exergetic values of the net electricity and steam were found to be 20.66 MW and 50.41 MW, respectively, while the chemical exergy content of the produced lactic acid was determined at 112.84 MW. The overall exergy destruction of the system stood at 207.69 MW. The contribution of the steam generation, as the main subunit wasting exergy, to the overall exergy destruction of the system was found to be 63.31%. Furthermore, lactic acid production subunit stood in the next rank in terms of exergy destruction, contributing 16.30% of the overall exergy destruction of the biorefinery. The universal and functional exergetic efficiencies of the system were determined as 52.71% and 44.73%, respectively, while the normalized exergy destruction was calculated as 1.13.

Exergetic analysis of deep-bed drying of rough rice in a convective dryer

This research work was aimed at a detailed exergy analysis of paddy deep-bed drying process in a laboratory-scale convective dryer. The drying experiments were conducted at different inlet drying air temperatures and flow rates, and exergy loss, exergy destruction, exergy efficiency, improvement potential rate and sustainability index were investigated. Normalized exergy destruction of the process ranged from 1.67 to 7.46. Exergy efficiency of the drying process and drying chamber was specified to be in the ranges of 5.10–29.41% and 32.64–67.75%, respectively. The results indicated that applying higher drying air temperatures resulted in higher exergy efficiencies. The obtained improvement potential rates showed that the process is highly capable of ameliorating exergy performance improvement. The sustainability index of drying process ranged from 1.05 to 1.42, augmenting with the increase in the air temperature.

A novel emulsion fuel containing aqueous nano cerium oxide additive in diesel–biodiesel blends to improve diesel engines performance and reduce exhaust emissions: Part II – Exergetic analysis

This survey was aimed at determining exergy-based sustainability parameters of a single cylinder DI diesel engine in response to various fuel blends and engine loads at a fixed engine speed of 1000rpm. Engine tests were conducted at four engine loads (25%–100%) using a 95% diesel+5%biodiesel blend (B5) emulsified with water (3, 5, and 7w/w%). All the prepared emulsions were stabilized with a 2:1 combination of Span 80 and Tween 80 (overall 7.5w/w% surfactant). Two levels of cerium oxide nanoparticle concentrations (0 and 90ppm) were also applied. The results showed that engine load and fuel type profoundly affected the exergy-based sustainability indices of the engine. Generally, increasing engine load steadily decreased exergy efficiency, while normalized exergy destruction declined by up to 75% under full load condition. Among the fuel blends prepared, the B5 blend containing 3w/w% water and 90ppm cerium oxide nanoparticles (B5W3m) showed the best exergy-based sustainability parameters at all the studied engine loads as its respective performance approached that of the basal petro-diesel. More specifically, among the fuel blends prepared the highest exergetic efficiency and the lowest normalized exergy destruction at full load condition were achieved using the selected fuel blend at 28.26% and 1.52, respectively. These values stood at 28.36% and 1.69 for neat diesel, respectively. However, the selected emulsified fuel blend represented a remarkably better environmental performance compared with diesel fuel. In conclusion, the B5W3m fuel blend might be effectively applied as substitute to mineral diesel fuel without any change in the existing engine structure.

Multi-objective exergy-based optimization of a continuous photobioreactor applied to produce hydrogen using a novel combination of soft computing techniques

This work was aimed at proposing a flexible and reliable framework based on combination of three soft computing techniques, i.e., artificial neural network, genetic algorithm, and fuzzy systems for multi-objective exergetic optimization of continuous photobiohydrogen production process from syngas by Rhodospirillum rubrum bacterium. To this end, artificial neural network (ANN) coupled with fuzzy clustering method (FCM) to model exergetic outputs on the basis of input variables. The outputs of modeling system were then fed into a novel optimization approach developed by hybridizing additive linear interdependent fuzzy multi-objective optimization (ALIFMO) and the elitist non-dominated sorting genetic algorithm (NSGA-II). The optimization was carried out to minimize the normalized exergy destruction and maximize the rational and process exergetic efficiencies, simultaneously. The solutions of the proposed approach were also compared with conventional fuzzy multi-objective optimization procedure with independent objectives. Overall, the modeling system predicted the exergetic parameters of photobioreactor with a coefficient of determination higher than 0.90. Furthermore, the optimization approach suggested syngas flow rate of 13.35 mL/min and agitation speed of 383.34 rpm as the best operational condition by considering the preferences of process exergy efficiency, rational exergy efficiency, and normalized exergy destruction, respectively. This condition could yield the normalized exergy destruction of 1.56, process exergetic efficiency of 21.66%, and rational exergetic efficiency of 85.65%. The obtained results showed the superiority of the proposed approach over the conventional fuzzy method in optimizing the complex biofuel production systems.

Multi-objective exergetic optimization of continuous photo-biohydrogen production process using a novel hybrid fuzzy clustering-ranking approach coupled with Radial Basis Function (RBF) neural network

This work was focused on the development of a novel hybrid fuzzy clustering-ranking approach coupled with radial basis function (RBF) neural network for the optimization of the key operational parameters for hydrogen production from photo-fermentation. The bioconversion of syngas to hydrogen via water-gas shift (WGS) reaction was carried out using the light-dependent microorganism Rhodospirillum rubrum and acetate as the sole carbon source. The RBF neural network was used to correlate exergetic outputs (normalized exergy destruction as well as rational and process exergetic efficiencies) to two exogenous input variables (culture agitation speed and syngas flow rate). The developed RBF model was interfaced with the proposed hybrid fuzzy clustering-ranking algorithm to simultaneously maximize the rational and process exergetic efficiencies and minimize the normalized exergy destruction. Moreover, the conventional fuzzy optimization algorithm was applied in order to assess the capability of the proposed approach over the existing methods for multi-objective optimization of complex biofuel production systems such as a continuous photobioreactor with respect to the sustainability and productivity issues. Once the development of the objective functions were carried out using RBF Neural Networks, the proposed algorithm was able to identify the optimum exergetically-sustainable operational parameters of carbon monoxide fermentation to biohydrogen with 20 clusters. This corresponded to the syngas flow rate of 13.68 mL/min and culture agitation speed of 348.62 rpm yielding the process exergetic efficiency of 16.46%, rational exergetic efficiency of 91.59%, and normalized exergy destruction of 2.14. The predicted optimum values by the proposed algorithm in this study were more suitable compared with the conventional fuzzy method. Therefore, the novel algorithm developed in this study may be a promising approach for navigation of the most cost-effective and environmental friendly operational parameters for fermentative hydrogen production from pilot and large scale photobioreactors.

Exergy-based sustainability assessment of continuous photobiological hydrogen production using anaerobic bacterium Rhodospirillum rubrum

The present research work was aimed at analyzing a continuous photobioreactor for hydrogen production from syngas via Rhodospirillum rubrum from exergetic point of view. Exergy balance equation was developed for the investigated photobioreactor based on both conventional exergy and eco-exergy concepts in order to determine its exergetic performance parameters. Two new exergetic criteria called the process exergetic efficiency and normalized exergy destruction were also introduced in order to maximize biohydrogen production and minimize resources destruction. The results obtained showed that the process exergy efficiency, a decision making parameter, varied between 14.71–22.90% and 14.71–22.84% using the conventional exergy and eco-exergy approaches, respectively. The minimum normalized exergy destruction was found to be 0.97 using both approaches. In general, no noticeable changes were observed in the conventional exergetic and eco-exergetic performance parameters of the bioreactor over 540 h of continuous operation. Nevertheless, eco-exergetic analysis owing to the inclusion of the work of information embedded in the genomes of living organisms is still recommended for improving the design features of photobioreactors for hydrogen production. The outcomes of the present study could provide practical insights to engineers and researchers dealing with analysis, design, development, optimization, and improvement of large-scale photobioreactors for hydrogen production.

Effect of phosphate concentration on exergetic-based sustainability parameters of glucose fermentation by Ethanolic Mucor indicus

In this work, a holistic exergetic-based framework was developed to assess the sustainability and productivity of a batch bioreactor used for ethanol production by ethanolic fungus Mucor indicus. Analyses were carried out in order to identify the most exergetically-sustainable concentration of phosphorous compound to produce ethanol and biomass. The microorganisms were aerobically cultivated using glucose as carbon source at various phosphate concentrations ranging from 0.0 to 7.5 g/L. The results obtained showed that the exergetic parameters of the fermentation process were remarkably influenced by the concentration of phosphate. Generally, the findings achieved revealed 3.5 g/L phosphate concentration as the most optimal fermentation condition from the exergetic point of view. Under this condition, the process exergetic efficiency and normalized exergy destruction as decision making parameters were found to be 53.42% and 0.48 kJ/kJ product, respectively. Moreover, the rational and process sustainability indexes for this concentration were determined at 3.92 and 2.15, respectively. The developed framework could be easily transplanted to evaluate the renewability of various lab-scale biofuel production processes to meet the goals laid forth for sustainable development.

An exergetically-sustainable operational condition of a photo-biohydrogen production system optimized using conventional and innovative fuzzy techniques

The aim of the present study was to perform an exergy-based multi-objective fuzzy optimization of a continuous photobioreactor applied for biohydrogen production from syngas via the water-gas shift reaction by Rhodospirillum rubrum. For this purpose, the conventional and innovative fuzzy optimization techniques coupled with multilayer perceptron (MLP) neural model to optimize the main exergetic performance parameters of the photobioreactor. The MLP neural model was applied to correlate three dependent variables (rational and process exergy efficiencies and normalized exergy destruction) with two independent variables (syngas flow rate and agitation speed). The developed MLP model was then interfaced with three different multi-objective fuzzy optimization systems with independent, interdependent, and locally modified interdependent objectives. The optimization process was aimed at maximizing the rational exergy and process efficiencies, while minimizing the normalized exergy destruction, simultaneously. Generally, the innovative locally modified interdependent objectives fuzzy system showed a better optimization capabilities compared with the other two fuzzy systems. Accordingly, the optimal syngas photo-fermentation for biohydrogen production in the continuous bioreactor corresponded to the agitation speed of 383.34 rpm and syngas flow rate of 13.35 mL/min in order to achieve the normalized exergy destruction of 1.56, rational exergy efficiency of 85.65%, and process exergy efficiency of 21.66%.

Using exergy to analyse the sustainability of fermentative ethanol and acetate production from syngas via anaerobic bacteria (Clostridium ljungdahlii)

In this study, exergetic performance assessment of ethanol and acetate fermentation in a batch bioreactor using Clostridium ljungdahlii under various syngas pressures in the range of 0.8–1.8atm was carried out. The exergetic parameters of the bioreactor were determined using both conventional exergy and eco-exergy concepts in order to select the most sustainable and productive operational conditions. In general, a significant difference was found between the exergetic values of the process using the conventional exergy and eco-exergy approaches. Overall, the eco-exergy concept showed to be a more appropriate approach for analysing and optimizing energy conversion systems including living organisms due to the incorporation of the work of the genetic information embodied in the genomes of microorganisms. The lowest overall normalized exergy destruction was found to be 49.96kJ/kJ Ac+EtOH and 40.54kJ/kJ Ac+EtOH at initial pressure of 1.8atm using the conventional exergy and eco-exergy concepts, respectively. Accordingly, this condition would be recommended to be applied in pilot- or industrial-scale bioreactors. Finally, the employed approach herein could be used to shed light on on-going attempts to improve the performance of the bioreactors for biofuel production considering the sustainability and productivity perspectives.

On the exergetic optimization of continuous photobiological hydrogen production using hybrid ANFIS–NSGA-II (adaptive neuro-fuzzy inference system–non-dominated sorting genetic algorithm-II)

The aim of this work was to exergetically optimize the performance of a continuous photobioreactor for hydrogen production from syngas via water gas shift reaction by Rhodospirillum rubrum. To achieve this, a new multi-objective hybrid optimization technique was developed by coupling the elitist NSGA-II (non-dominated sorting genetic algorithm) with the ANFIS (adaptive neuro-fuzzy inference system) to optimize the operational conditions of the photobioreactor. The syngas flow rate and culture agitation speed were independent variables, while rational and process exergy efficiencies as well as normalized exergy destruction were dependent variables. The ANFIS was used to establish an objective function for each dependent variable individually based on the independent variables. The developed ANFIS model was then utilized by the NSGA-II approach to find the optimal operating conditions simultaneously leading to the highest rational and process exergy efficiencies and the lowest normalized exergy destruction. Consequently, the best operating conditions for the photobioreactor were extracted using a Pareto optimal front set consisting of seven optimum points. Accordingly, syngas flow rate of 13.34 mL/min and culture agitation speed of 383.33 rpm yielding process exergy efficiency of 21.66%, rational exergy efficiency of 85.64%, and normalized exergy destruction of 1.55 were found as the best operating conditions.

Exergy Analysis as a Tool for Decision Making on Substrate Concentration and Light Intensity in Photobiological Hydrogen Production

AbstractThis study was devoted to the comprehensive thermodynamic modeling of biohydrogen production using the light‐dependent bacterium Rhodospirillum rubrum. Conventional exergy and eco‐exergy concepts were employed for decision making on the carbon source concentration and light intensity. Several thermodynamic indicators were applied to identify optimal experimental variables with respect to the sustainability issues. The results obtained using both concepts were remarkably different from each other because of the incorporation of the work of the information embodied in the genomes of the living microorganisms considered in the eco‐exergetic computations. However, both analyses identified 1 g L−1 substrate concentration and 1000 lux light intensity as optimal fermentation conditions. Under these conditions, the normalized exergy destruction as a decision making parameter was 179.7 and 171.54 kJ kJ −1 using the conventional exergy and eco‐exergy concepts, respectively.

Exergy analysis of biohydrogen production from various carbon sources via anaerobic photosynthetic bacteria (Rhodospirillum rubrum)

In this study, exergy analysis of batch biohydrogen production through WGS (water–gas shift) reaction using an anaerobic photosynthetic bacteria Rhodospirillum rubrum was carried out for the first time. Various carbon sources including formate, acetate, malate, glucose, fructose, and sucrose were applied to support microbial growth in the presence of CO-rich syngas. The microorganisms utilized carbon monoxide and produced molecular hydrogen concurrently. The process was analyzed based on both conventional exergy and eco-exergy concepts for determining the exergetic parameters i.e., exergy destruction and exergy efficiency. Unlike the exergy efficiency, the exergy destruction based on the eco-exergy concept was remarkably lower than what obtained via the conventional exergy theory. Minimum normalized exergy destruction values of 189.67 and 181.40 kJ/kJ H2 were achieved for acetate as substrate using the exergy and eco-exergy approaches, respectively. In better words, acetate was identified as the most appropriate carbon source for biohydrogen production from the exergy point of view. Finally, the findings of this study confirmed that exergy analysis could be employed as an adaptable framework to determine and compare the renewability of biological hydrogen production using different routes in order to decide on the most suitable approach and conditions.