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Landfill leachate is a contaminated liquid containing an extensive range of organic and inorganic pollutants. Therefore, it could pose serious risks to the environment by polluting soil and groundwater. In this study, the application of magnetic CuFe2O4/RGO nanocatalyst for persulfate activation was investigated in mature landfill leachate remediation. In this regard, CuFe2O4/RGO was synthesized through straightforward sonochemical co-precipitation method, and was characterized by different analysis approaches (FTIR, XRD, EDX, Raman, VSM, and TEM). A reaction mechanism for persulfate activation in this process was elucidated, and the influence of initial pH, persulfate concentration and catalyst loading were evaluated on the efficiency of the process as well. Under optimum condition, final COD, NH3-N and color removal efficiencies reached to 65.1%, 65%, and 58% respectively, and regenerability of the catalyst was assessed during five consecutive runs. Furthermore, a comparison between functional groups of the initial and degraded leachate samples was performed using ATR-FTIR analysis. In this way, the degraded products after the oxidation process were studied, and the results showed that the treatment system was capable of degrading aromatic compounds to aliphatic ones.

The main goal of this research is to investigate the CO2 solubility and regeneration of aqueous solution of methyl diethanolamine (MDEA) and monoethanolamine (MEA) mixed by 1,5-Diamino-2-methylpentane (DAMP) as absorption promoter. In the first step, the absorption capacity and rate of the solutions are experimented in an isothermal batch reactor at various MDEA to MEA ratios, and DAMP concentration. The CO2 cyclic capacity of solutions is investigated by performing CO2 absorption at 30 °C and solvent regeneration at 70 °C. Then, the effect of DAMP concentration on the boiling point, density, mass loading and pH of aqueous mixture of MEA and MDEA is analyzed. The experimental results show that increasing DAMP concentration in the samples increases the absorption rate and capacity of base solution. The results of cyclic absorption and desorption tests show that there is not a considerable difference between cyclic capacity of the base and promoted solutions at 70 °C.

The main goal of this research is to investigate absorption rate and capacity of carbon dioxide in aqueous mixtures of DAMP(1,5-Diamino-2-methylpentane)+MDEA(Methyl-diethanolamine), DAMP+MEA(Monoethanolamine), DAH(1,6-Diaminohexane)+MDEA and DAH+MEA in an isothermal stirred batch reactor. The experiments are designed in temperature ranging from 30 to 50 °C, at low pressure and various molar ratios. The results show that although there is an interaction between diamine and conventional amines at low diamine concentration, after a minimum absorption point, increasing DAMP and DAH concentration in conventional amines increases absorption capacity of solutions in all temperatures. Then, a rate equation is developed to predict absorption rate of aqueous solution of DAMP based on Zwitterion mechanism. The kinetic constant and order of reaction are determined considering the absolute difference between measured and predicted rate as the objective function. The results show that the reaction is first order with respect to amine concentration and third order in overall.

Concerns over environmental pollution and ever-increasing energy demand have urged the global community to tap clean-burning fuels among which dimethyl ether is a promising candidate for contribution in the transportation sector. Direct dimethyl ether synthesis from syngas, in which methanol production and dehydration take place simultaneously, is arguably the preferred route for large scale production. In this study, direct dimethyl ether synthesis is proposed in an industrial dual-type fluidized bed reactor. This configuration involves two fluidized bed reactors operating in different conditions. In the first catalytic reactor (water-cooled reactor), the synthesis gas is partly converted to methanol after being preheated by the reaction heat in the second reactor (gas-cooled reactor). A two-phase generalized comprehensive reactor model, comprised of the flow in three different regimes is applied and a smooth transition between flow regimes is provided based on the probabilistic averaging approach. The optimal operating conditions are sought by employing differential evolution algorithm as a robust optimization strategy. The dimethyl ether mole fraction is considered as the objective function during the optimization. The results show considerable dimethyl ether enhancement by 16% and 14% compared to the conventional direct dimethyl ether synthesis reactor and dual-type fixed bed dimethyl ether reactor arrangements, respectively.

In process systems, analysis of control degrees of freedom (CDOF) is a primary step for control structure design. CDOF is equal to the maximum number of variables that can be independently manipulated by the control system. This study develops a novel approach for systematic computation of CDOF through the identification of topological characteristics of process flow diagrams (PFDs). The analysis has a top-down approach in which the main set of potential manipulated variables (MVs) is decomposed into internal and boundary subsets, then CDOFs for the internal and boundary MVs are determine separately. In order to develop the method, three assisting terms have been introduced: (1) circuit, (2) bypass, and (3) route. The suggested formula computes both dynamic and steady state CDOF using basic information obtained from the plant’s PFD. Applicability and reliability of the proposed method have been successfully checked for various case studies ranging from simple units to complex process plants.

Display Omitted Various reservoir models have been detected using novel coupled MLP-DWT model.Our proposed model detected reservoir models from training set with TCA=95.37%.Our coupled model recognized reservoir models from test set with TCA of 94.34%.The proposed model has been validated using synthetic and actual noisy real data. Well testing analysis is performed for detecting oil and gas reservoir model and estimating its associated parameters from pressure transient data which are often recorded by pressure down-hole gauges (PDG). The PDGs can record a huge amount of bottom-hole pressure data, limited computer resources for analysis and handling of these noisy data are some of the challenging problems for the PDGs monitoring. Therefore, reducing the number of the recorded data by PDGs to a manageable size is an important step in well test analysis. In the present study, a discrete wavelet transform (DWT) is employed for reducing the amount of long-term reservoir pressure data obtained for eight different reservoir models. Then, a multi-layer perceptron neural network (MLPNN) is developed to recognize reservoir models using the reduced pressure data. The developed algorithm has four steps: (1) generating pressure over time data (2) converting the generated data to log-log pressure derivative (PD) graphs (3) calculating of the multi-level discrete wavelet coefficient (DWC) of the PD graphs and (4) using the approximate wavelet coefficients as the inputs of a MLPNN classifier. Sensitivity analysis confirms that the most accurate reservoir model predictions are obtained by the MLPNN with 17 hidden neurons. The proposed method has been validated using simulated test data and actual field information. The results show that the suggested algorithm is able to identify the correct reservoir models for training and test data sets with total classification accuracies (TCA) of 95.37% and 94.34% respectively.

This research focuses on carbon dioxide absorption using aqueous solution of 1,5-diamino-2-methylpentane (DAMP) in an isothermal batch reactor. Equilibrium CO2 loading by the proposed aqueous solution is measured at the temperature range 30–50 °C, CO2 partial pressure range 5–150 kPa, and different solvent concentrations. To prove the performance of the proposed diamine to capture CO2, absorption and desorption capacity, absorption rate and cyclic capacity of 1,5-diamino-2-methylpentan is measured and compared with MEA and MDEA. The experimental results show that the equilibrium CO2 loading of 1,5-diamino-2-methylpentane, MDEA and MEA are 0.86, 0.43 and 0.54 at 30 °C, respectively. Lower volatility of 1,5-diamino-2-methylpentane compared to MEA results in a lower solvent loss through CO2 removal process. Applying the proposed amine in the absorption towers can decrease solvent circulating rate and required equilibrium stages.

Landfill leachate, is a contaminated liquid consisted of different toxic and hazardous pollutants which could penetrate the ground and reach the groundwater, leading to the soil and water resources pollution. Hence, it is regarded as a substantial risk to the environment. Accordingly, in this work, a sustainable and efficacious sulfate radical-based oxidation approach using persulfate and heterogeneous magnetic CuFe2O4 nanocatalyst was suggested to treat municipal stabilized landfill leachate. In this study, CuFe2O4 catalyst was synthesized through the straightforward sonochemical co-precipitation method, and it was characterized by FTIR, EDX, VSM, XRD, and TEM analysis techniques. The dominant mechanism of the degradation process was discussed in detail, and the influence of various operating conditions such as pH, persulfate dosage and catalyst loading was investigated on the treatment performance of the process based on COD, ammonia-nitrogen (NH3-N) and color removal efficiencies. Under optimized condition (pH = 2, persulfate amount = 5 g/L and catalyst dosage = 1.5 g/L), the treatment process showed acceptable efficiency removals (COD, NH3-N and color removal efficiencies of 57%, 71%, and 63%, respectively). Moreover, performance of the current system was compared with performances of distinct persulfate-based and CuFe2O4-based treatment systems. The reusability of the catalyst was also evaluated over the sequential treatment runs, and the catalyst showed high reactivity over four repeated cycles. To prove the capability of the treatment process in pollutants degradation ATR-FTIR analysis of the leachate samples before and after the oxidation process was conducted, and the results showed that aromatic compounds were converted to aliphatic components after the oxidation.

Modified Claus process is the most important process that recovers elemental sulfur from H2S. The thermal stage of sulfur recovery unit (SRU), including the reaction furnace (RF) and waste heat boiler (WHB), plays a critically important role in sulfur recovery percentage of the unit. In this article, three methods including kinetic (PFR model), equilibrium and equilibrium-kinetic models have been investigated in order to predict the reaction furnace effluent conditions. The comparison of results with industrial data shows that kinetic model (for whole the thermal stage) is the most accurate model for simulation of the thermal stage of the industrial split-flow SRU. Mean absolute percentage error for the considered kinetic model is 4.59 %. For the first time, the consequences of considering heat loss from the reaction furnace on calculated molar flows are studied. The results show that considering heat loss only affects better prediction of some effluent molar flow rates such as CO and SO2, and its effect is not significant on the results. Eventually the effects of feed preheating on some important parameters like sulfur conversion efficiency, H2S to SO2 molar ratio and important effluent molar flows are investigated. The results indicate that feed preheating will reduce the sulfur conversion efficiency. It is also noticeable that by reducing the feed temperature to 490 K, H2S/SO2 molar ratio reaches to its optimum value of 2.

Pressure transient behavior is among the most important information for characterizing a reservoir, forecasting its future performance, and designing an appropriate recovery scheme. Although a continuous real-time monitoring of reservoir bottom-hole pressure has become a routine task in intelligent wells, complete extraction of the potential information from these valuable sources of data may not be achieved by using traditional interpretation methods. Deconvolution transforms the pressure transient data related to the wells with variable production rates into an equivalent constant rate pressure data with duration equal to the whole duration of the multi-rate test i.e., unit step response. This technique can reveal high valuable information over a distance from the wellbore which may be several orders of magnitude greater than the radius of investigation of individual flow periods. In the present study, a robust and practical deconvolution methodology is developed for extracting the unit step response (USR) from synthetic, noisy and incomplete pressure transient histories pertaining to multi-rate data. Our proposed scheme calculates the USR from those multi-rate well testing data which may contain high levels of noises in both the flow rate and pressure data. A coupled wavelet transform/superposition theorem is the basis of the proposed method. The algorithm has shown an excellent performance for revealing reservoir/boundary models and their associated parameters.

Pressure transient response (PTR) of a hydrocarbon reservoir to the alteration of production or injection rate can be computed through solution of its associated diffusivity equation. The PTR is likely the most important data for characterizing a hydrocarbon reservoir, forecasting its future productive performance, designing the appropriate stimulation scenario, and optimizing its management activity. The aim of this study is to simulate the effect of a time varying boundary flux on the PTR of a bounded gas reservoir using a simple and straightforward approximate scheme. The physical model can be viewed as a finite homogeneous reservoir experiencing a constant production rate at its inner boundary and an exponential flux in its outer boundary. Since direct handling of the time varying boundary condition is often hard, it makes a traditional solution applying Laplace transform or/and its inverse very tedious and time consuming. Therefore in this study the orthogonal collocation (OC) method which is elegant in its simplicity and efficient in its application is employed for simulating the PTR of the considered model. Reliability of the OC methodology is verified by comparing its result with an exact analytical solution for the closed reservoir. The proposed OC method has predicted the exact analytical solution with the absolute average relative deviation (AARD %) of 0.36%. Thereafter a general approximate solution is proposed to describe the PTR of gas reservoir with the time varying flux in the external boundary. The effect of boundary type, boundary flux, and boundary radius on the characteristic shape of the derivative graph has been investigated. The numerical results indicate that the PTR of a gas reservoir that experiencing time varying flux in its outer border can be successfully simulated using this approximate approach.

Recently, we have proposed a new formulation approach for control degree of freedom (CDOF) analysis of process systems. This formula interrelates the CDOF and elements of a process flow diagram (PFD). The formulation is accurate, easily applicable by process engineers, and needs little prior knowledge of the process under study. This paper describes further about this new formulation and its advantages by applying it to batch and cyclic processes. The results demonstrate the correctness of the method in determining CDOF for all case studies at both steady state and dynamic conditions. The CDOF for “reheat regenerative Rankine” and “vapor absorption refrigeration” cycles have been determined to be 9 and 8, respectively. The steady state CDOF values for the Rankine and refrigeration cycles have been calculated as 8 and 4, respectivley. Also the CDOF for the process of “heat pump integrated with batch distillation column” is 4 that verifies the suggested formula of CDOF. The method also gives beneficial insights about the manipulated variables (MVs) in a control system.

Steam reforming of methane (SRM) is an important stage of hydrogen production. Using a membrane reactor (MR) to separate the produced H2positively affects CH4conversion by shifting the equilibrium. This H2removal increases the risk of coke formation in the process. In this study, the influence of different parameters such as Damkohler’s number (Da) and permeation number (θ) on CH4conversion and H2recovery are investigated. In order to find the optimum condition for this MR in which CH4conversion, H2Recovery are maximized and the risk of coke formation is minimized, the elitist non-dominated sorting genetic algorithm (NSGA-II) is employed to achieve the Pareto front in a three objective space. The single optimal solution is selected from Pareto front by TOPSIS decision making method. In the optimized condition methane conversion and hydrogen recovery are improved about 19.8% an 6.8%, respectively. Also, the risk of coke formation in the MR is reduced.

This chapter deals with the modeling approach toward membrane reactors, making a short overview on the most significative findings in the specialized literature. In detail, 1-D, 2-D, and 3-D models are analyzed, pointing out the role of such parameters as the membrane permeability mechanism and hydrogen flux, reaction kinetics, and heat and mass transport inside the reactor and within the catalyst pellets, able of influencing the accuracy of the model.

The main objective of this study is utilization of recurrent neural networks to categorize pressure derivative plots of well-testing data into various reservoir models. The training and test data have been generated through an analytical solution of commonly used reservoir models. The accuracy of the designed recurrent neural networks has been examined by the simulation test data and actual field data. The accuracy of the developed recurrent neural networks has been compared to a multilayer perceptron neural network. The results indicate that the recurrent neural networks can identify the correct reservoir models from test data with an accuracy of 98.39%, while multilayer perceptron neural networks represent an accuracy of 95.83%.

The well testing technique has been frequently used in order to identify hydrocarbon reservoir models and estimate the associated parameters such as permeability, skin factor, etc. The analysis of well test data acquired from gas condensate reservoirs is basically different from oil and dry gas reservoirs often exhibiting a complex behavior due to the formation of condensate inside the reservoir. The first step in well test analysis is the detection of reservoir model and its boundaries usually performed through trial-and-error procedures. Previous investigations indicate that the radial composite model is the best feasible model for well test analysis of gas condensate reservoirs. The radial composite model refers to those reservoirs consisting of two separate regions: (1) a circular inner zone with the well at the center, and (2) an infinite outer zone. The best multi-layer perceptron (MLP) configuration is also selected through evaluating the accuracy criteria of various developed MLP networks i.e., measuring the mean relative (MRE) and mean square errors (MSE). The total classification accuracies (TCAs) of two methods used in this study indicate that the coupled MLP clustering model (with a TCA equal to 93.3%) has a better performance than that of the single MLP (with a TCA of 88.65%).

In this article, at first, a quantitative structure–property relationship (QSPR) model for estimation of limiting diffusion coefficients of hydrocarbon liquids, D AB 0 , is developed. Particle swarm optimization variable selection (PSOvs) method, together with multi-linear regression (MLR) model, selects suitable descriptors from a pool of 1124 predefined descriptors. The MLR model is trained based on a data bank of 345 experimental binary diffusivities of hydrocarbons in infinite dilution solutions. The objective function of the optimizer for descriptor selection is the multi-variant RQK function. In addition, an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) are developed as two nonlinear black-box estimators of D AB 0 based on the descriptors obtained from PSOvs algorithm. The estimation results of these three QSPR models (PSO-MLR, ANN and ANFIS) are compared with five semi-empirical correlations. Furthermore, using 402 experimental binary diffusion coefficient data at different concentrations and temperatures, another multi-layer perceptron network (MLPN) is constructed to predict the diffusion coefficients of binary hydrocarbon liquid systems at various concentrations and temperatures. The input variables of this network are D AB 0 , D BA 0 , and mole fraction of the component A. Average absolute relative deviations (AARD) of the developed MLP-QSPR model for training, test, and whole data are respectively, 5.86%, 7.79%, and 6.32%. Indeed this model has less than 5% AARD for more than 50% of the data points. Also, the results of the MLP model designed to estimate the binary diffusivities of concentrated hydrocarbon mixtures show that the average absolute relative error for the whole of test and training data is 6.3%. The comparison of the developed MLP model with experimental data and some of semi-empirical correlations indicates that the suggested modeling scheme based on QSPR and MLP method has very good accuracies for estimation/prediction of liquid hydrocarbon diffusivity not only in infinite dilution but also at concentrated solutions.

Residual aluminium ion in water after the coagulation step in water and wastewater treatment units is a health issue that requires an effective solution. A kind of ceramic foam is developed that can be utilized to remove aluminium ions from water. Ceramic material was coated in slurry form over polyurethane sponge that is used as a structure. Full factorial experiments were performed to study the effects of polymeric sponge number, number of immersions in the slurry, sintering temperature, and soaking time on ceramic foam properties. Fabrication conditions are modeled and adjusted such that the optimum ceramic foam attained good adsorption capacity while having necessary compression strength to be used as packing. The optimum ceramic foam was obtained to be a 20 sponge number with four times immersions in slurry and the sintering occurred at 860 °C in 135 min. Characterizations of optimum ceramic foam were performed by SEM imaging and measuring specific surface area, porosity, density, and determining adsorption isotherm. The maximum measured adsorption capacity of ceramic foam at 25 °C was 3.0639 mgg−1. Regeneration tests were performed using a 0.001 M HCl solution. It is observed that 76 % of adsorption capacity can be maintained by the first cycle, and after ten times of the adsorption/desorption cycles, this value reaches to 19.7 %.

In this study, pretreatment of sugarcane bagasse and subsequent enzymatic hydrolysis is investigated using two categories of pretreatment methods: dilute acid (DA) pretreatment and combined DA-ozonolysis (DAO) method. Both methods are accomplished at different solid ratios, sulfuric acid concentrations, autoclave residence times, bagasse moisture content, and ozonolysis time. The results show that the DAO pretreatment can significantly increase the production of glucose compared to DA method. Applying k-fold cross validation method, two optimal artificial neural networks (ANNs) are trained for estimations of glucose concentrations for DA and DAO pretreatment methods. Comparing the modeling results with experimental data indicates that the proposed ANNs have good estimation abilities.

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.

Dimethyl ether (DME) is traditionally produced by methanol dehydration in an adiabatic reactor. Recently, a more economical method has been proposed to produce DME in a reactor in which methanol production and dehydration take place simultaneously on a bi-functional catalyst. In the present study, the design and optimization of an industrial scale fixed bed reactor for the direct synthesis of DME from syngas are investigated. A steady state, pseudo-homogeneous model has been applied to simulate the proposed reactor. At first, the preliminary design of the reactor is done based on the reactor design heuristics for industrial reactors. Then, using differential evolution (DE) algorithm as a fast and efficient optimization method, the tentative reactor operating conditions and its internal configuration are optimized. The objective of the optimization is to maximize DME production in each tube of the reactor. The number of tubes, feed inlet and coolant water temperatures are considered as decision variables of the optimization algorithm. At the optimum conditions, the reactor size decreases due to increase of CO conversion and DME productivity in each tube. The results show that the proposed optimum reactor is more economical for large-scale production of DME in comparison to the conventional industrial DME reactor.

Since in foreseeable future DME as a clean-burning fuel with versatile applications will play a significant role in the transportation sector, the least improvement in its production process which can increase the production capacity is economically favorable. In this study, an industrial dual-type reactor is designed and optimized by DE algorithm for increasing DME production and also overcoming the reaction equilibrium limitations of the direct DME synthesis. The proposed reactor configuration is composed of two fixed bed reactors: (1) the water-cooled reactor and (2) the gas-cooled reactor. The syngas feed is preheated with the hot exit product from the first reactor (water-cooled), and is entered into the first reactor. The reactors are simulated by means of a one-dimensional steady-state heterogeneous model. The simulation results indicate that the best flow pattern for the proposed configuration is the counter-current mode. Maximizing the outlet mole fraction of DME from the second reactor is the objective of the optimizer. Seven operating and geometrical parameters of the system are considered as the decision variables. The results show that the proposed configuration can enhance DME production capacity about 60 ton/day in comparison to conventional industrial DME reactor which operates based on the indirect DME synthesis process.

The present contribution aims to enhance dimethyl ether (DME) production rate as well as hydrogen as clean-burning fuels and versatile applications. In this regard, a thermally coupled membrane configuration (TCMDR), which is able to produce hydrogen and DME simultaneously, is proposed. Here, direct DME synthesis from syngas and cyclohexane dehydrogenation reaction are coupled and occur in the exothermic and endothermic compartments, respectively. The dehydrogenated product (hydrogen) is pushed through the wall of the third partition, which is a Pd/Ag membrane composite, in order to overcome the equilibrium constraints of dehydrogenation reaction. Moreover, the optimal operating conditions are sought by aid of differential evolution (DE) algorithm as a powerful optimization technique. During the optimization step, the sum of carbon monoxide and cyclohexane conversions along with the hydrogen mole fraction in the permeation side is considered as the objective function. Finally, the TCMDR behavior is examined based on the achievements during the optimization procedure and a one-dimensional steady-state heterogeneous model. The results show considerable DME enhancement in the TCMDR by 10.3% and 11.4% compared with the conventional direct DME synthesis reactor (CDR) and thermally coupled DME reactor (TCDR) arrangements and at the same time the amount of endothermic raw material drops about 120.3 kmol/h.

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.

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...

Hydrocarbon reservoirs are complex heterogeneous media, and their parameters are usually identified indirectly through using well testing techniques. The well testing is basically conducted through creating a flow disturbance in the well and recording the related response of the bottom-hole pressure. This technique provides the needed data for quantitative analysis of the reservoir parameters and reservoir characterization. The well testing method consists of two stages: (1) the recognition of the reservoir model, and (2) the parameter estimation. The aim of this study is to apply the artificial neural network (ANN) to the recognition of the reservoir model. The structure of the neural network used in this work is a multi-layer perceptron (MLP) network. The required training and test data sets have been generated by using the analytical solutions of commonly-used reservoir models. Eight important reservoir models considered in this study include homogenous and dual porosity reservoir models with different outer boundaries such as no flow, constant pressure, infinite acting and single sealing fault boundaries. The mean relative errors (MRE) and the mean square errors (MSE) of the test data have been used for determining the number of neurons in the hidden layer. The required CPU time for the training of the proposed network has also been utilized for selection of the most suitable training algorithm. A two-layer MLP network with twelve neurons in its hidden layer has been designed as the best configuration. The scaled conjugate gradient method has been chosen as the training algorithm. The performance of the proposed ANN has been examined by the actual field data in addition to simulation noisy and noiseless data sets. The results indicate that the proposed two-layer MLP network can identify the reservoir models with an acceptable accuracy.

Dimethyl ether (DME) has gained wide interest in chemical industry regarding its use as a multi-source, multi-purpose fuel either for diesel engines or as a clean alternative for liquefied petroleum gas (LPG). The direct synthesis of DME from syngas would be more economical and beneficial in comparison to the indirect process via methanol dehydration. In this study, one type of the multifunctional auto-thermal reactors (the recuperative one) is selected in which the direct synthesis of dimethyl ether (DME) is coupled with the catalytic dehydrogenation of cyclohexane to benzene in a two fixed bed reactor separated by a solid wall, where heat is transferred across the surface of tube. Steady-state, heterogeneous, one-dimensional model has been used to describe the performance of this novel configuration. Both co-current and counter-current operating modes are investigated and the simulation results are compared with the available data of a pipe-shell fixed bed reactor for direct DME synthesis which operates at the same feed conditions. In addition, the influence of the molar flow rate of exothermic and endothermic stream on the reactor performance is also investigated. The results suggest that coupling of these reactions could be feasible and beneficial and the co-current mode has got better performance in DME and hydrogen production. In order to establish the validity and safety handling of the new concept, an experimental proof is required.

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.

In this study, a multilayer perceptron (MLP) network is proposed to predict the thermal conductivity (λ) of an electrolyte solution at atmospheric pressure, over a wide range of temperatures (T) and concentrations (x) based on the molecular weight (M) and number of electrons (n) of the solute. The accuracy of the proposed artificial neural network (ANN) was evaluated through performing a regression analysis on the predicted and experimental values of various aqueous solutions, some of which were not used in the network training. The comparison of the developed MLP network to other correlations recommended in the literature indicates that the proposed neural network outperforms other alternative methods, with respect to accuracy as well as extrapolation capabilities. Besides, others’ conductivity correlations are usually suggested for a specific electrolyte solution and a limited range of temperatures and concentrations, while such limitations do not exist for the proposed MLP network.

This paper proposes a hierarchical artificial neural network (HANN) for isolating the faults of the Tennessee-Eastman process (TEP). The TEP process is the simulation of a chemical plant created by the Eastman Chemical Company to provide a realistic industrial process for evaluating process control and monitoring methods The first step in designing the HANN is to divide the fault patterns space into a few sub-spaces through using fuzzy C-means clustering algorithm. For each sub-space of fault patterns a special neural network has been trained in order to diagnose the faults of that sub-space. A supervisor network has been developed to decide which one of the special neural networks should be triggered. In this regard, each neural network in the proposed HANN has been given a specific duty, so the proposed procedure can be called Duty-Oriented HANN (DOHANN). The neuromorphic structure of the networks is based on multilayer perceptron (MLP) networks. The simulation of Tennessee-Eastman (TE) process has been used to generate the required training and test data. The performance of the developed method has been evaluated and compared to that of a conventional single neural network (SNN) as well as the technique of dynamic principal component analysis (DPCA). The simulation results indicate that the DOHANN diagnoses the TEP faults considerably better than SNN and DPCA methods. Training of each MLP network for the DOHANN model has required less computer time in comparison to SNN model. This is because of structurally simpler MLPs used by the developed DOHANN method.

Dimethyl ether (DME) as a clean fuel has attracted the interest of many researchers from both industrial communities and academia. The commercially proven process for large scale production of dimethyl ether consists of catalytic dehydration of methanol in an adiabatic fixed-bed reactor. In this study, the industrial reactor of DME synthesis with the accompanying feed preheater has been simulated and controlled in dynamic conditions. The proposed model, consisting of a set of algebraic and partial differential equations, is based on a heterogeneous one-dimensional unsteady state formulation. To verify the proposed model, the simulation results have been compared to available data from an industrial reactor at steady state conditions. A good agreement has been found between the simulation and plant data. A sensitivity analysis has been carried out to evaluate the influence of different possible disturbances on the process. Also, the controllability of the process has been investigated through dynamic simulation of the process under a conventional feedback PID controller. The responses of the system to disturbance and setpoint changes have shown that the control structure can maintain the process at the desired conditions with an appropriate dynamic behavior.

In this study, a feedforward three-layer neural network is developed to predict binary diffusion coefficient (DAB) of gases at atmospheric pressure over a wide range of temperatures based on the critical temperature (Tc), critical volume (Vc) and molecular weight (M) of each component in the binary mixture. The accuracy of the method is evaluated through a test data set not used in the training stage of the network. Furthermore, the performance of the neural network model is compared with that of well known correlations suggested in the literature. The results of this comparison show that our developed method outperforms other correlations, with respect to accuracy as well as extrapolation capabilities.

Steam distillation process improves oil recovery processes involving steam injection up to 50%. Due to its immense effect on oil recovery, several attempts have been made to simulate this process experimentally and theoretically. Since detailed crude oil data is rarely available, a model should be presented to predict the distillate rate with minimum entry parameters. For this purpose, a Multi-Layer Perceptron (MLP) network is used in this research as a new and effective method to simulate the distillate recoveries of 16 sets of crude oil data obtained from literature. API, viscosity, characterization factor and steam distillation factor are input parameters of the network while distillate yield is the result of the model. Thirteen sets of data were used for training the network and three remaining sets were used to test the model. Comparison between the developed MLP model, Equation of State (EOS)-based method and Holland–Welch correlations indicates that the errors of the MLP model for training and test data sets are significantly lower than that of those methods. Also, the MLP network does not require oil characterization, which is a necessary and rigorous step in EOS and Holland–Welch methods.

A feedforward three-layer neural network is proposed to predict conductivity (k) of pure gases at atmospheric pressure and a wide range of temperatures based on their critical temperature ( T c ), critical pressure ( P c ) and molecular weight (MW). The accuracy of the method is evaluated and tested by its application to experimental conductivities of various gases which some of them are not used in the network training. Furthermore, the performance of the proposed technique is compared with that of conventional recommended models in the literature. The results of this comparison show that the proposed neural network outperforms other alternative methods, with respect to accuracy as well as extrapolation capabilities. Besides, conventional conductivity correlations are usually used for a limited range of temperature and components while the network method is able to cover a wide range of temperatures and substances.

Prediction of gas thermal conductivity is crucial in the heat transfer process. In this article, we develop a novel method to estimate conductivities of binary gaseous mixtures at atmospheric pressure. The method is a neural network scheme consisting of two consecutive multilayer perceptrons (MLPs). The first MLP estimates pure component conductivities as a function of critical temperature, critical pressure, molecular weight, and temperature. The conductivities calculated in the first MLP as well as molecular weights of both compounds and mole fraction of the light components are fed to the second MLP to predict the thermal conductivity of the mixture. The proposed model was trained and tested through a large set of experimental data over wide ranges of temperatures, compositions, and substances. Comparing the test and training results indicates that the accuracy of the neural model is remarkably better than other alternative methods proposed in the literature. Conventional conductivity correlations requ...

Steam distillation could improve the oil recovery efficiency during the steam injection enhanced oil recovery process. Because of its immense effects on oil recovery, it is important to investigate the main parameters of steam distillation as well as the effects of oil and reservoir properties during this thermal process. In this work, the simulation of batch steam distillation is performed on 18 sets of crude oil found in the literature. The developed model is highly compatible with respect to the input oil properties that can also characterize the oil with minimum entry. The calculated distillates were compared to the experimental data, and the results show an average relative error of 13.74% for 15 sets of crude oil data, each calculated at 20 different points. According to this study, the superheat conditions of steam and the amount of light oil fractions have the greatest effect on the distillation yields, while the steam saturation conditions have less considerable effects. It was also found that th...

An optimal temperature profile is determined for a methanol synthesis reactor of LURGI type. The temperature profile is estimated so that methanol production rate in the reactor outlet will be maximized. First, the reactor is simulated based on heterogeneous one- and two-dimensional models. The comparison of the simulation results and plant data shows that the heterogeneous one-dimensional model can reliably be used for determining optimal temperature profile. Since optimal temperature profile for reversible exothermic reaction in tubular reactors is a decreasing function of reactor length, the technique of control variable parameterization is used for determining optimal temperature profile in a methanol reactor. In this way, a third order polynomial is considered for the temperature profile and the polynomial coefficients are as decision variables. The optimization is based on a Quasi-Newton's method (BFS technique), and the objective function is methanol flow rate at the reactor outlet. The results of ...

The control of nonlinear multivariable continuous stirred tank reactors with time varying unknown parameters is a challenging area. This paper is concerned with a framework that combines a nonlinear observer for estimating unknown and time varying kinetic rates in reactor and a generic model control algorithm to manage the set points variations of the outputs. This nonlinear adaptive control scheme can perform well in the presence of external disturbances and it is easy to tune the design parameters. A series of computer simulations are provided to verify the ability of the proposed approach.

Dimethyl ether (DME) is a green fuel that commercially produced in an adiabatic fixed bed reactor by methanol dehydration. In the present work, a shell and tube fixed bed reactor is modeled and optimized for DME production. The reactor is modeled based on mass and energy conservation equations as well as auxiliary equations. In order to estimate the DME production and temperature profile along the reactor, a one dimensional heterogeneous model consist of a set of nonlinear differential and algebraic equations has been solved numerically. Also, The DME production in the isothermal reactor is maximized by adjusting the optimal temperature distribution along the reactor using genetic algorithm. Then, the performance of the proposed isothermal reactor is compared with industrial adiabatic fixed bed reactor. Results showed the higher DME production rate and methanol conversion in the optimized reactor.

Dual porosity model refers to those reservoirs which have two different media. The interporosity flow coefficient (λ), and storativity ratio (ω). Well testing analysis is used to estimate reservoir parameters that are used in the reservoirs description. Pressure derivative plots corresponding to different value of λ and ω, are dissimilar. Derivative plots are used to design a model based on ANN to estimate λ and ω. In this study the capability of Artificial Neural Network to estimate λ and ω from well testing data has been investigated. Well testing data for dual porosity reservoir have been generated and converted to derivative plots. The best configuration of ANN has been selected by a trial and error procedure through applying different training algorithms and changing the number of neurons in the hidden layer. Using this procedure, a two-layer ANN model has been found as an efficient tool to estimate ω and λ. The trained ANN has been validated using the test data not been used in the training data set. The results have shown that the ANN is capable of estimating λ and ω using derivative plot obtained from the reservoir simulation as well as the information obtained from the literatures.

Riser-slugging is a flow regime that can occur in multiphase pipeline-riser systems, and is characterized by severe flow and pressure oscillations. Reducing undesired slugging effects can have great economic benefits. Recently, control methods have been proposed to conquer slugging flow problems in pipeline risers. The advantages of using a control system are that it can be installed on existing oil and gas production facilities with no need for expensive equipment and no significant pressure drop is imposed to the system.In this work, a predictive control system based on Neural Network (NN) model of process is developed for handling and suppressing riser-slugging. An ANN model of the plant is used to predict future response of the nonlinear process. Storkaas dynamic model (Storkaas and Skogestad,2002) is employed for the process simulation. Comparing the results of this research to that of others, indicates that the proposed neural model predictive controller makes a significant improvement in the setpoint tracking especially for higher step change in the setpoint value.