Search

Since narrow-spectrum antibiotics specifically target the infection-causing organism, their negative side effects are reduced compared with their broad-spectrum counterparts. However, the design of these narrow-spectrum antibiotics requires accurate knowledge about drug targets in different microorganisms and their selectivity. Constraint-based metabolic models can provide this required knowledge using a mathematical framework for in-silico cell metabolism analysis and rewiring. Furthermore, competing against infectious pathogens, especially drug-resistant organisms, is more efficient by targeting multiple targets in each individual microorganism. Here, we combined the idea of synthetic lethality with selective drug targeting to obtain multi-target and organism-specific potential drug candidates for six different microorganisms and their various combinations. By considering each organism as targeted, conserved, or not included, we obtained 665 different cases for single essential reactions as well as double, triple, and quadruple synthetic lethal reaction sets. We found that conserving even one microorganism while attacking some targets reduces the number of potential cases tremendously. The number of solutions depends on how genomically far or close the microorganisms are in the phylogenetic tree. Furthermore, we statistically investigated how these potential drug targets attack different pathways in our studied cases, which reveals the importance of key routes such as cell envelope biosynthesis, glycerophospholipid metabolism, membrane lipid metabolism, and nucleotide salvage pathway.

In the current research, a mathematical model is developed to simulate a commercial ethylene dichloride thermal cracker assuming pseudo-steady state condition. The considered reaction network includes 19 elementary and reversible reactions, 14 molecules, and 6 radical species. To prove the validity of the proposed model, the simulation results are compared with the available plant data. The results show that the coke build-up and deposition on the inner surface of the coil tube increases the heat transfer resistance during the process run time and has a negative effect on the production rate and pressure drop. Afterward, a sensitivity analysis is performed to investigate the effects of the feed and firebox temperatures on the ethylene dichloride cracking severity and selectivity. The results show that increasing the firebox and feed temperatures enhance the vinyl chloride monomer production rate. In the next step, a dynamic optimization problem is formulated and considered to calculate the optimal dynamic trajectory of the feed and furnace temperatures to achieve the maximum vinyl chloride monomer production rate. The programmed optimization problem is solved by the genetic algorithm method. The results show that applying the optimal trajectory of the decision variables can enhance the VCM production rate by 2.5%.

Miniaturization of the electronic devices and dissipation of the heat generated during the operations within such equipment are of the most important issues in the design of these components. In the current experimental study, it is shown that deploying a cylindrical microchannel heat sink (CMHS) results in a superior efficiency for cooling the equipment when sinusoidally wavy fins are applied with an innovative structure in its geometry rather than the straight ones. The heat sinks considered include 80 straight/wavy microchannels with hydraulic radius of 589 μm and fins of 600 μm in both width and height located on a cylinder. In order to further enhance the heat transfer, alumina nanoparticles were used at three different concentrations, namely 0, 0.1, and 0.3 mass%. In the experiments performed using the base fluid, it is demonstrated that with increase in the Reynolds number in the range of 200–1000, the Nusselt number of the sinusoidal microchannels rises by 6–40% in comparison with that of straight microchannels. In addition, the use of the nanofluid instead of the base fluid further improves the heat transfer coefficient in the both cylindrical microchannels with straight and sinusoidal fins, such that an increase in the concentration of the nanofluid enhances the Nusselt number accordingly. Comparison between the thermal performance of the sinusoidal wavy fins to that of the straight ones in the same conditions (the size and geometry of the heat sink) illustrates that greatly improved thermal performance is achieved utilizing the sinusoidal fins. Also, the application of alumina nanofluids improves the thermo-hydraulic performance index (THPI) of the straight and the wavy CMHSs. The performance index range of 1.05–1.07 and 0.82–1.07 was obtained, respectively, for the 0.3 mass% nanofluid flow in the straight and wavy CMHSs.

The main objective of this research is to improve the CO2 solubility and absorption rate of conventional alkanolamines including monoethanolamine (MEA) and methyldiethanolamine (MDEA) by 1,4–diaminobutane (DAB) as a promoter. Typically, the presence of two primary functional groups, lack of strict hindrance, and low molecular weight make DAB an efficient solvent to absorb CO2. In the first step, the absorption kinetic of DAB is investigated theoretically and a rate equation is proposed based on the zwitterion mechanism. In the second step, the equilibrium capacity of MEA and MDEA is promoted by DAB experiment in an isothermal stirred batch reactor in the pressure range 20–100 kPa, temperature range 303–313 K, and promoter volume fraction 0–10%. Then, the effective loading and regenerability of prepared samples are investigated by continuous cyclic absorption and regeneration in the designed reactor. The experimental results show that increasing DAB concentration in the MEA and MDEA as base solutions increases the CO2 absorption rate and equilibrium capacity. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

The multidrug resistance of numerous pathogenic microorganisms is a serious challenge that raises global healthcare concerns. Multi-target medications and combinatorial therapeutics are much more effective than single-target drugs due to their synergistic impact on the systematic activities of microorganisms. A synthetic lethal (SL) is a set of non-essential targets (i.e., reactions or genes) that prevent the proliferation of a microorganism when they are “knocked out” simultaneously. To facilitate the identification of SLs, we introduce Rapid-SL, a new multimodal implementation of the Fast-SL method, using the depth-first search algorithm. The advantages of Rapid-SL over Fast-SL include: (a) the enumeration of all SLs that have an arbitrary cardinality, (b) a shorter runtime due to search space reduction, (c) embarrassingly parallel computations, and (d) the targeted identification of SLs. Targeted identification is important because the enumeration of higher order SLs demands the examination of too many reaction sets. Accordingly, we present specific applications of Rapid-SL for the efficient targeted identification of SLs. In particular, we found up to 67% of all quadruple SLs by investigating about 1% of the search space. Furthermore, 307 sextuples, 476 septuples, and over 9,000 octuples are found for Escherichia coli genome-scale model, iAF1260.

CO2 removal from the process and/or fuel gas streams, due to its undeniable significance, is one of the most important research areas in the field of separation and purification of industrial gases, in a way that it is mainly considered as a commitment. The existing standards and the required characteristics of the treated process and/or fuel gases for using in downstream processes and applications, on one hand, and preventing the emission of CO2 as the most important greenhouse gas to the atmosphere, on the other hand, are the most prominent among the commitments. CO2 removal from syngas as the main building block in most chemical and petrochemical processes, as well as biogas as a promising and renewable resource for replacing fossil fuels, is critical. This chapter provides a brief introduction to syngas and biogas synthesis methods and their effects on the amount of CO2 present in the both produced gases. Although the ultimate aim of CO2 separation from biogas and syngas may be different, similar approaches can be applied to achieve this goal. Hence, in the following, these techniques and their characteristics are examined in a categorized manner. The purpose of the current review is to provide a comprehensive understanding of each approach.

Separation and removal of carbon dioxide from process gases is an important issue from both process and environmental perspectives. CO2 removal may be considered as an industrial commitment according to the industrial standards and required characteristics of treated gases for downstream processes. CO2 removal is critically important due to the issue of greenhouse effect and global warming. However, traditional methods only contribute to separation and removal of CO2 from mainstreams. Unless the removed CO2 is somehow stored, eventually, it will be released to the atmosphere. Hence, any traditional method can be viewed as a temporary solution. In other words, current approaches are not sustainable and CO2 capture from the atmosphere may also remain as a challenge for the future of the planet. Among the (potentially) available techniques for CO2 capturing, methods based on the natural biological transformation processes would be more promising, sustainable, and effective. As an example, during the photosynthesis process, carbon fixation reactions result in CO2 capture (removal) and its conversion to carbohydrates and biomass. This chapter presents a problem statement regardingthe status of biological methods for CO2 capture. In addition, the carbon fixation pathways are briefly reviewed. Finally, the challenges of industrialization and applying these approaches in real scale are discussed.

This study examines time-varying adsorption of aqueous tetracycline (TC) by metal-organic framework, MOF-5. The employed MOF-5 is firstly synthesized under room temperature condition. Its structural, chemical, and physical features are then determined applying FTIR, XRD, SEM, TGA, N2 adsorption, and TEM characterization techniques. Cubic arrangement, 84.3% crystallinity, up to 450 °C thermal stability and approximately 2510 m2/g BET surface area with the both micro- and meso-cavities are its revealed characteristics. The impacts of the operative factors (adsorbent dosage, contact time, initial TC concentration, initial pH and temperature) on TC removal efficiency are examined following a two-step experimental design procedure (two-level categorical screening followed by response surface methodology for correlative model development). Additionally, to define the most appropriate condition/conditions, model-based optimization is implemented and verified in practice (more than %96 TC removal is achieved). Further thermodynamic and kinetic assessments respectively signify the both physisorption (Langmuir adsorption isotherm with maximum capacity of 233 mg/g) and chemisorption (pseudo-second-order model) mechanisms in this adsorption system. To sum up, MOF-5 as a suitable adsorbing agent will possibly have significant effects on pharmaceutical wastewater treatment and can be considered for advanced studies as an appropriate candidate regarding practicable remediation techniques.

Purpose Wheat straw is an agricultural waste which can be used as a cost effective animal feed. However, high hemicellulose and phytic acid content in wheat straw prevents it as a primary feed choice. Utilization of wheat straw in solid-state fermentation may result in wheat straw valorization and enzyme production. In this study, phytase production in solid-state fermentation of wheat straw using Aspergillus ficuum and valorization of wheat straw were evaluated. Methods A two-step experimental design procedure was employed for screening and optimization of influencing factors on phytase production. Effects of different nutritional and environmental factors were investigated by one factor at the time method (OFATM). To reach higher amounts of phytase, response surface methodology (RSM) was employed to optimize phytase production as a function of three of the most effective factors. Results Optimization of the significant parameters resulted in an increase in the phytase activity from 0.74 ± 0.12 to a maximum of 16.46 ± 0.56 Units per gram dry substrate (U gds−1). The high degree of the fungal phytase activity on wheat straw resulted in the decrease in phytic acid content by 57.4%, as compared to the untreated sample. Scanning electron microscopy (SEM) and FTIR structural analysis showed intensive fungal growth on wheat straw, and partial removal of hemicelluloses, lignin and phytic acid. Conclusion The study demonstrated the feasibility of wheat straw utilization in solid-state fermentation using Aspergillus ficuum toward the production of phytase and valorization of wheat straw as an animal feed.

Thanks to their remarkable features (i.e. extremely high surface areas and porous structures with tunable pore sizes), MOFs are promising materials for adsorption of different traceable pollutants from wastewaters. In this study, MIL-101(Cr), is synthesized and examined as a capable adsorbent for removal of diazinon from aqueous media in a continuous fixed-bed system. The properties of the MIL-101(Cr) are determined applying SEM, XRD, FTIR, TGA, HRTEM and BET characterization techniques. Accordingly, mesoporous octahedral structure, 96% crystallinity, up to 550 °C thermal stability and about 2600 m2/g BET surface area are obtained. The impacts of the operative factors (the initial pH, the initial diazinon concentration, the volumetric flow rate and the bed depth) on diazinon removal efficiency are further examined. Also, EDS and FTIR tests are employed to explore the diazinon adsorption on the adsorbent surface. It is obtained that neutral pH is the best choice to achieve the maximum removal efficiency (92.5%). The highest bed height (1 cm) contributes to the longest breakthrough time (13 min) while increase in volumetric flow rate and initial diazinon concentration lead to decrease of exhaustion time. Utilizing the experimental data, Langmuir isotherm is the best fitted adsorption isotherm model. Also, the Thomas model predicts the system’s behavior better than Adams–Bohart one. It is the first time that MIL-101(Cr) is applied for adsorption of diazinon from aqueous solutions. As a consequence, it will undoubtedly have noteworthy impacts on agricultural wastewater treatment and can be considered for advanced research as an appropriate agent regarding practical remediation processes.

Electrocoagulation process is an electrochemical method to remove variety of contaminants (i.e., pesticides) from water and wastewater effluents. The current research examines the elimination of propiconazole by electrocoagulation technique. For this aim, after the preliminary screening tests (using factorial design) for the identification of the significant and remarkable factors, response surface methodology based on central composite design is employed to develop a correlative model for the efficiency of the electrocoagulation process. Furthermore, simultaneous optimization of the operational process variables [electrolysis duration (10–60 min), propiconazole initial concentration (10–50 mg/L), current density (2.5–12.5 mA/cm2), and solution conductivity (0.5–2.5 mS/cm)], is carried out to find the most appropriate strategy. The screening of the prime considered factors indicates that pH is not a significant variable and consequently is excluded from further deliberations. A quadratic correlative model is prepared, and model-based optimization is further performed and experimentally verified. The maximum removal efficiency (79.83%) is attained regarding the following process factors: the propiconazole initial concentration of 25.93 mg/L, the time duration of 36.44 min, the current density of 10.95 mA/cm2, and the solution conductivity of 2.44 mS/cm. The results demonstrate that through electrocoagulation process with proper operational variables, the amounts of propiconazole in wastewater effluents can adequately get lowered to acceptable levels.

The main aim of this study is to examine the effects of exploitation of synthetic combination of indigenous and exogenous bacteria on biodegradation of South Pars gas refineries’ (SPGRs) oily sludge contaminants in liquid and soil media during a 60-day time period. To the best of authors’ knowledge, this is the first time that the capabilities of Staphylococcus spp., Lysinibacillus fusiformis, and Citrobacter amalonaticus for degradation of petroleum compounds are uncovered. One of the most important achievements of the combined consortium, is the additional reduction of the broth surface tension due to biosurfactants production. Results show that the combined consortium has the highest biodegradation ability in 1% (w/v) oily sludge supplied mineral salt and soil media by respectively 81.0% and 71.2% through biostimulation + bioaugmentation techniques. It is also observed that adding exogenous bacteria to indigenous ones contributes to increase of oily-sludge degradation by 11.4% in liquid media and 17.6% in soil. Finally, addition of exogenous bacteria resulted in significant increase of removal percentage of alkanes, aromatics, and asphalthene + resins contaminants in liquid and soil media.

The present study focused on producing and characterizing a type of biosurfactant (BS) derived from lactic acid bacteria (LAB) and its potential applications in pharmaceutical and food industries due to the preference of employing nonpathogenic organisms in bioprocesses. To this aim, several screening approaches were applied to identify an efficient BS-producing strain from a set of LAB, and Pediococcus dextrinicus SHU1593 was selected as the most operative one. The BS produced by P. dextrinicus was isolated and structurally characterized as a lipoprotein with an approximately equal ratio of lipids (~52% (w/w)) and proteins (47% (w/w)). It reduced the surface tension (ST) of phosphate-buffered saline (PBS) from 72.80 ± 0.10 to 39.01 ± 0.32 mN/m. The results also indicated the potential of developing low-cost strategies aimed at the production of efficient LAB-derived BSs which are structurally and quantitatively similar to the ones obtained from conventional media. Finally, given the physical and functional characterization (i.e. critical micelle concentration (CMC), emulsification index (%E

Recently, there has been great interest in the field of metabolic engineering. Engineered microorganisms such as bacteria can be exploited to synthesize value-added bioproducts. Conversely, optimal genetic manipulations to reach to operative engineered cells is very costly; hence, regarding whole-cell functionalities, a number of proficient simulation algorithms have been developed and widely applied for systems-level analyses, and as a consequence, systems metabolic engineering. However, most of the currently-used in silico algorithms are usually time-consuming. In other words, it is requisite that computer scientists gain prior knowledge of molecular biology and biochemistry to be able to develop more effective algorithms in this field. Here, in the current study, we regard the metabolic network of interest as a general graph to present algorithmic perspective of the complex metabolic systems. A cell's metabolism is modeled in the form of a metabolic network. This way, the major problems in the field of metabolic engineering can be tackled using graph theory and graph mining algorithms. For this purpose, we reconstruct the metabolic network as a general graph, redefine the metabolic engineering problems accordingly, and finally, solve one of them as a sample.

In this study, the effect of changes in operating conditions is considered following a three–step procedure. Firstly, the process is simulated based on the design data for model validation and model–based optimization. The presented best-fitted kinetic and thermodynamic models in the literatures are utilized to analyze the trends and kinetic features related to methanol synthesis. The variations in the operating conditions such as the inlet temperature and the mole fractions of CO and CO 2 significantly affect the methanol production rate. Low operating performance of the heat exchangers and the alterations in operating conditions contribute to increase of the amount of purge gas of the process from its predicted quantity in the design condition. Since it has been anticipated that the purge gas may rise, a no–flow flare (zero flaring) has been designed and the excess of purge gas is burnt in this flare. Secondly, the process is simulated based on the operating data to calculate the streams conditions. Thirdly, the analysis and statistical optimization are performed. Applying response surface methodology (RSM), the operating conditions of this plant are optimized via simulator–based experimental design in order to maximize methanol production. RSM is a collection of mathematical and statistical techniques useful for modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response. Consequently, the results of the statistical analysis prove that the methanol production rate increases by 7% applying the optimal operating conditions.

This study introduces a method of quantitative structure–property relationship (QSPR) to evaluate/predict surface tension (ST) of binary-liquid mixtures using a large-data-set over the (254.85–443.25 K) temperature range, and the whole concentration span. To construct and establish an accurate, comprehensive, reliable, and predictive model, the subsequent procedure is followed. Firstly, a set of 8200 data points is collected. The data set belongs to experimental surface tension values attained for 469 binary-liquid mixtures (relevant to 156 chemical components) at different temperatures and various concentrations. Secondly, to select the optimum and the most effective descriptors, a repeated search technique based on genetic algorithm (GA) and partial least squares (PLS) is applied. Thirdly, contrasting with the ST of the binary mixtures, the best construction of multi-layer perceptron neural network (MLPNN) is employed for examination of the nonlinear behavior of the selected descriptors other than temperature and mole fraction of the light components (empirical descriptors). Comparing the results to the experimental data confirms the appropriate and well-meaning accuracy of the ultimate model (4.89% mean relative error). This would be of significant importance as the proposed model, which directly uses the molecular structures without requiring any experiment, has a proper precision while the accuracy of empirical or thermodynamic-based models is limited and depends on specific model factors and/or other parameters obtained from experimental data.

The aim of the present study was to investigate the effect of ultrasonic treatment (25 kHz) on biosurfactant production by Lactobacillus plantarum ATCC 8014. The impacts of the ultrasonication (with a frequency of 25 kHz and power of 7.4 W for 30 min time duration) were examined at different stages of the fermentation process to obtain the optimum stimulation instant(s). The optimum scenario was found to be one-time sonication at the 12th hour of fermentation which can be beneficial from an economic point of view (compared with multiple applications of sonication). Ultrasonic treatment at this time resulted in enhancement of the productivities of biomass (4.5 g/L) and biosurfactant (2.01 g/L) which was almost 1.3 times higher than those of the non-sonicated control samples. According to our results, it was clearly observed that glucose consumption increased after ultrasonic treatment representing the improved substrate uptake and progression of the cellular metabolism. Furthermore, the transmission electron microscopic images immediately after sonication clarified the pore formation on the cell surfaces. The results also indicated the enhancement of plasma membrane permeability of the sonicated cells. Fourier transform infrared spectroscopy and scanning electron microscopy coupled with energy dispersive x-ray spectroscopy analyses also disclosed respectively no structural differences before and after ultrasonic exposure in the produced biosurfactant and bacterial cell membrane. The biosurfactant was characterized to be a mixture of carbohydrate (28%), protein (23%) and lipid (specified by gas chromatography-mass spectrometry) known as glycolipoprotein. The sustainable critical micelle concentration and the stability of the synthesized biosurfactant can feature its potential applicability in various processes in the food and pharmaceutical industries.

Accurate experimental determination of the effective thermal conductivity (ETC) of porous reservoir rocks (especially under high temperature and pressure conditions) is a difficult problem and often a time-consuming and costly process. This study firstly examines the ability of the theoretical and empirical correlations for estimating the air saturated sandstone, quartz and limestone ETCs based on the models available in the literature. Optimal values of constant parameters of these correlations are found using the genetic algorithm (GA) technique. Empirical correlations have acceptable accuracy; however, they are not applicable in wide ranges of temperature and/or pressure. Also, each equation is dependent on the composition of the porous rock. In other words, they are not generalized correlations. The ability of artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) as two generalized models are also investigated utilizing 872 experimental data points for a wide range of pressure and temperature. Temperature, pressure, porosity and bulk density are considered as the inputs of the mentioned models. An optimal topology of multi-layer perceptron neural network model (MLPNN) is determined via 10-fold cross-validation method. The total average absolute relative deviation (AARD (%)) of the developed ANN and ANFIS models for estimation of ETC are obtained 2.91% and 3.80%, respectively. The results show that the optimal ANN model is able to estimate ETC with the higher accuracy than the other correlations.

Biological production of acetone, butanol, and ethanol (ABE) utilizing inexpensive substrates has been extensively studied from economic and environmental points of view for its contribution to sustainable energy production and pollution reduction. The current research, firstly examines the impacts of different Clostridium basal media (CBMs) on the growth of Clostridium acetobutylicum NRRL B-591 and biosynthesis of ABE. The influence of various salinities (i.e. 0.01, 5, 10, 20 and 40 g/L) on the bacterium activities for the determined optimal CBM is subsequently investigated. The results reveal that although the bacterium can effectively form ABE in CBM-3, it produces much more acid in the other ones. The evaluations demonstrate that glucose concentration has significant effects on the maximum bacterial growth and ABE production rates. Hence, the bacterium requires at least 20 g/L glucose to shift from acid-producing phase to solvent-biosynthesis one. Comparing to the well-studied strain (Clostridium acetobutylicum ATCC 824), the employed strain demonstrates significant capability to synthesize ABE using inexpensive basal medium (CBM-3). Sodium chloride shows destructive effect on C. acetobutylicum NRRL B-591 growth and consequently ABE production. An increase of salinity from 0.01 to 20 g/L, prolongs the lag phase from 30 to 93 h and ABE production from 89 to 15 mM. Correspondingly, the bacterium cannot grow under 40 g/L salinity due to cytoplasm dehydration and cell lysis. The obtained results help us to better understand and analyze the behavior and functionalities of the bacterium of interest for further research especially regarding improvement of the performance of the associated bioprocesses.

Rhamnolipids (RLs) produced by the opportunistic human pathogen Pseudomonas aeruginosa are considered as potential candidates for the next generation of surfactants. Large-scale production of RLs depends on progress in strain engineering, medium design, operating strategies, and purification procedures. In this work, the rhlAB genes extracted from a mono_RLs_producing strain of P. aeruginosa (ATCC 9027) were introduced to an appropriate safety host Pseudomonas putida KT2440. The capability of the recombinant strain was evaluated in various media. As a prerequisite for optimal medium design, a set of 32 experiments was performed in two steps for screening a number of macro-nutritional compounds. In the experiments, a two-level fractional factorial design resolution IV was followed by a two-level full factorial one. By means of this approach, it was observed that glycerol, yeast extract, and peptone have significant positive influence on recombinant RLs production while the yeast extract/peptone two-factor and glycerol/yeast extract/peptone three-factor interactions have considerable negative effects. A wide range of variation from 0 to 570 mg/l was obtained for RLs production during the screening experiments indicating the importance of medium optimization. The results point out the opportunity for possible higher yields of RLs through further screening, mixture/combined mixture designs, and high-cell-density cultivations.

Carrying out reaction and separation simultaneously in a reactive dividing wall batch distillation column batch RDWC in the case of ethyl acetate synthesis provides the possibility of separating both products and increasing the equilibrium reaction conversion. Overcoming the known azeotrope conditions, high purity for ethyl acetate and decreasing the batch time compared to simple reactive batch distillation are the advantages of this configuration. The corresponding dynamic simulation is carried out by simultaneously solving the model-associated system of differential and algebraic equations. In this study, the optimal values of the vapour and liquid split ratios are considered as the decision variables in order to maximize the amount of ethyl acetate accumulated during batch time. The optimization strategy is implemented inspired by response surface methodology in which an optimal surface is fitted to the collected data set using differential evolution (DE). The optimal surface relevant algebraic equatio...

In this theoretical study, a framework for simulation and optimization of diauxie-involved fed-batch fermentative succinate production is proposed. In the proposed method, for the first time, diauxie is employed as a considerable phenomenon to decrease the level of undesired byproduct during the fermentative process. For this aim, an engineered mutant strain of Escherichia coli K-12, termed AB3, is considered. A hybrid-neural modeling framework is constructed based on dynamic flux balance analysis (DFBA) in which aerobic-condition-associated diauxie phenomenon (consumption of produced acetate) is included. The DFBA model is built using an in silico genome-scale metabolic network reconstruction with deletions of pfl , adhE , and ldhA genes. The intracellular description of carbon metabolism is simulated by a multilayer-perceptron (MLP) network to prepare the hybrid-neural model. First, model parameters are calculated by dynamic optimization. Then, a two-step bioprocess is considered in which a fed-batch aerobic step is followed by an anaerobic one with time-varying exponential feeding profiles. Optimal operating policies are determined using differential evolution method. The obtained strategy significantly increases the production of succinate compared to acetate (the major undesired byproduct). The proposed method can be used to study the potential of mutant strains for producing valuable metabolites in time-varying scenarios. In addition, it is shown that diauxie plays a key role in bioprocesses that can be used for preliminary purifications.

Surface tension as an important characteristic in much scientific and technological research is a function of liquid materials’ physical properties. Thus, it is desirable to have an accurate correlation between effective parameters and surface tension. This study investigates the applicability of artificial neural networks as an efficient tool for the prediction of pure organic compounds’ surface tensions for a wide range of temperatures. The experimental data gathered for training and verification of the network are related to a wide variety of materials such as alkanes, alkenes, aromatics, and sulfur, chlorine, fluorine, and nitrogen containing compounds. The most accurate network among several constructed configurations has one hidden layer with 20 neurons. The average absolute deviation percentage obtained for 1048 data points related to 82 compounds is 1.57%. The results demonstrate that the multilayer perceptron network could be an appropriate lookup table for the determination of surface tension as...

In this work, a reduced form of dynamic flux balance model based on artificial neural networks for batch and fed-batch fermentation of xylose-utilizing engineered Saccharomyces cerevisiae RWB 218 is developed. The intracellular description of carbon metabolism in the model is simulated by a multilayer-perceptron (MLP) network. First, this hybrid model is compared to the full mechanistic dynamic flux balance analysis (DFBA) in terms of accuracy and computational time regarding available experimental data on anaerobic batch cultivation. Afterwards, it is used in a model-based sequential dynamic optimization procedure in order to maximize ethanol productivity. The initial liquid volume charged in the bioreactor, the feed flow rates in aerobic and anaerobic conditions, the final batch time, and the switching time from aerobic to anaerobic conditions are considered as decision variables. Differential evolution (DE), as a robust and efficient optimization method, is employed to solve the problem for several glu...

Compared to some of the alternative fuel candidates such as methane, methanol and Fischer–Tropsch fuels, dimethyl ether (DME) seems to be a superior candidate for high-quality diesel fuel in near future. The direct synthesis of DME from syngas would be more economical and beneficial in comparison with the indirect process via methanol synthesis. Multifunctional auto-thermal reactors are novel concepts in process intensification. A promising field of applications for these concepts could be the coupling of endothermic and exothermic reactions in heat exchanger reactors. Consequently, in this study, a double integrated reactor for DME synthesis (by direct synthesis from syngas) and hydrogen production (by the cyclohexane dehydrogenation) is modelled based on the heat exchanger reactors concept and a steady-state heterogeneous one-dimensional mathematical model is developed. The corresponding results are compared with the available data for a pipe-shell fixed bed reactor for direct DME synthesis which is operating at the same feed conditions. In this novel configuration, DME production increases about 600 Ton/year. Also, the effects of some operational parameters such as feed flow rates and the inlet temperatures of exothermic and endothermic sections on reactor behaviour are investigated. The performance of the reactor needs to be proven experimentally and tested over a range of parameters under practical operating conditions.

This paper presents a study on optimization of a methanol synthesis and cyclohexane dehydrogenation in a thermally coupled reactor. A theoretical investigation has been performed in order to evaluate the optimal operating conditions and enhancement of methanol and benzene production in a thermally coupled reactor. Coupling energy intensive endothermic reaction systems with suitable exothermic reactions improves the thermal efficiency of processes and reduces the size of the reactors. In this work, the catalytic methanol synthesis is coupled with the catalytic dehydrogenation of cyclohexane to benzene in a heat exchanger reactor formed of two fixed beds separated by a wall, where heat is transferred across the surface of tube. A steady-state heterogeneous model of the two fixed beds predicts the performance of this novel configuration. The co-current mode is investigated and the optimization results are compared with corresponding predictions for a conventional (industrial) methanol fixed bed reactor operated at the same feed conditions. The differential evolution (DE), an exceptionally simple evolution strategy, is applied to optimize methanol and benzene synthesis coupled reactor considering methanol and benzene mole fractions as the main objectives. The simulation results have been shown that there are optimum values of initial molar flow rate of endothermic stream and inlet temperature of exothermic and endothermic sides to maximize the objective function. The optimization method has enhanced the methanol mole fraction by 3.67%. The results suggest that optimal coupling of these reactions could be feasible and beneficial. Experimental proof-of-concept is needed to establish the validity and safe operation of the novel reactor.

In this work, a novel radial-flow spherical-bed methanol synthesis reactor has been optimized using Differential Evolution (DE) algorithm. This reactor's configuration visualizes the concentration and temperature distribution inside a radial-flow packed bed with a novel design for improving reactor performance with lower pressure drop. The dynamic simulation of spherical multi-stage reactors has been studied in the presence of long-term catalyst deactivation. A theoretical investigation has been performed in order to evaluate the optimal operating conditions and enhancement of methanol production in radial-flow spherical-bed methanol synthesis reactor. The simulation results have been shown that there are optimum values of the reactor inlet temperatures, profiles of temperatures along the reactors and reactor radius ratio to maximize the overall methanol production. The optimization methods have enhanced additional yield throughout 4 years of catalyst lifetime, respectively.