We investigate the appearance of di?neutron bound states in pure neutron matter within the Brueckner?Hartree?Fock approach at zero temperature. We consider the Argonne v18 and Paris bare interactions as well as chiral two? and three?nucleon forces. Self?consistent single?particle potentials are calculated by controlling explicitly singularities in the g matrix associated with bound states. Di?neutrons are loosely bound, with binding energies below 1 MeV, but are unambiguously present for Fermi momenta below 1 fm?1 for all interactions. Within the same framework we are able to calculate and characterize di?neutron bound states, obtaining mean radii as high as
In this work, a two-dimensional model for a conventional packed-bed membrane reactor (CPBMR) is developed for the oxidative coupling of methane, which uses a nonselective porous membrane to continuously feed oxygen to the catalytic bed. The model incorporates radial diffusion and thermal conduction and assumes convective transport for the axial direction. In addition, two 10 cm long cooling segments for the CPBMR were implemented based on the idea of a fixed cooling temperature outside the reactor shell. The resulting model is discretized using two-dimensional orthogonal collocation on finite elements with a combination of Hermite polynomials for the radial and Lagrangian polynomials for the axial coordinate. The simulation study shows that it is necessary to make all transport coefficients dependent on local temperatures and compositions. This leads to a simulation with roughly 130,000 variables, which is then used to generate initial points for the optimization of the CPBMR stand-alone operation. In addition, inequality constraints and variable bounds are introduced so as to avoid potentially hazardous mixtures of methane and oxygen in both shell and tube as well as to keep the temperatures below levels stressing reactor materials (
A new approach for optimal experimental design has been developed to support the work of chemists and process engineers in determining reaction kinetics of complex reaction networks. The methodology is applied on sub-networks of the hydroformylation process of 1-dodecene with a Biphephos-modified rhodium catalyst in a DMF-decane thermomorphic solvent system (TMS). The isomerization and hydrogenation sub-networks are systematically analyzed with respect to parameter estimability. They are determined in a sequential approach using model-based optimal experimental design via perturbations with respect to temperature and synthesis gas pressure, and subsequently used to build up the reaction network. The focus of this contribution is the parameter estimation procedure at the very early investigation stage where model uncertainties are high. Sensitivities of sensitive parameters are increased while others are suppressed, which are carried over from the estimated sub-networks or structurally more difficult to determine. This subsequently leads to more reliable parameter estimations.
The hydroformylation of 1-dodecene on a rhodium-biphephos catalyst complex exploiting a thermomorphic multicomponent solvent system was studied experimentally in a batch reactor in order to describe the kinetics of the main and the most relevant side reactions. The formation of the active catalyst was studied in preliminary experiments. Based on a postulated catalytic cycle mechanistic kinetic models were developed considering isomerization, hydrogenation and hydroformylation reactions as well as the formation of not catalytically active Rh-species. The complex overall network was decomposed to support parameter estimation. The isomerization of 1-dodecene, the hydrogenations of iso- and 1-dodecene and the hydroformylations of iso-dodecene and 1-dodecene were investigated as a function of temperature, total pressure and partial pressures of carbon monoxide and hydrogen, respectively. These four sub-networks of increasing size and the total network were analyzed sequentially in order to identify kinetic models and to estimate the corresponding parameters applying model reduction techniques based on singular value decomposition combined with rank revealing QR factorization.
Cruz Bournazou MN, Junne S, Neubauer P, Barz T, Arellano-Garcia H, Kravaris C (2014) An approach to mechanistic event recognition applied on monitoring organic matter depletion in SBRs, AIChE Journal 60 (10) pp. 3460-3472
A fundamental practice in process engineering is monitoring the state dynamics of a system. Unfortunately, observability of some states is related to high costs, time, and efforts. The mechanistic event recognition (MER) aims to detect an event (defined as a change of the system with specific significance to the operation of the process) that cannot be directly observed but has some predictable effect on the dynamics of the systems. MER attempts to apply fault diagnosis techniques using mechanistic ?recognition? models to describe the process. A systematic method for building recognition models using optimal experimental design tools is presented. As proof of concept, the MER approach to detect organic matter depletion in sequencing batch reactors, measuring only ammonia, dissolved oxygen, and nitroxides is applied. The event, that is, consumption of organic matter to a level below 50 gCOD/m3, was successfully detected even though microbial activity is known to continue after organic matter depletion.
Jaao S, Sadjadi S, Godini H, Simon U, Arndt S, Görke O, Berthold A, Arellano-Garcia H, Schubert H, Schomäcker R, Wozny G (2012) Experimental investigation of fluidized-bed reactor performance for oxidative coupling of methane, Journal of Natural Gas Chemistry 21 (5) pp. 534-543 Elsevier
Performance of the oxidative coupling of methane in fluidized-bed reactor was experimentally investigated using Mn-Na2WO4/SiO2, La2O3/CaO and La2O3-SrO/CaO catalysts. These catalysts were found to be stable, especially Mn-Na2WO4/SiO2 catalyst. The effect of sodium content of this catalyst was analyzed and the challenge of catalyst agglomeration was addressed using proper catalyst composition of 2%Mn-2.2%Na2WO4/SiO2. For other two catalysts, the effect of Lanthanum-Strontium content was analyzed and 10%La2O3?20%SrO/CaO catalyst was found to provide higher ethylene yield than La2O3/CaO catalyst. Furthermore, the effect of operating parameters such as temperature and methane to oxygen ratio were also reviewed. The highest ethylene and ethane (C2) yield was achieved with the lowest methane to oxygen ratio around 2. 40.5% selectivity to ethylene and ethane and 41% methane conversion were achieved over La2O3-SrO/CaO catalyst while over Mn-Na2WO4/SiO2 catalyst, 40% and 48% were recorded, respectively. Moreover, the consecutive effects of nitrogen dilution, ethylene to ethane production ratio and other performance indicators on the down-stream process units were qualitatively discussed and Mn-Na2WO4/SiO2 catalyst showed a better performance in the reactor and process scale analysis.
The oxidative coupling of methane (OCM) is a promising alternative route to olefins that converts methane to higher hydrocarbons and open up a new feedstock for the oil based industry. However, due to yield limitations of available catalysts and high separation costs for conventional gas processing, the OCM process has not been applied yet in the industry. Starting with process simulation and sensitivity studies a flexible mini-plant was built in this research so as to demonstrate technical feasibility of an efficient OCM process, model validity and to study long term effects. By this means a concurrent engineering approach was applied for the whole process while investigating each unit parallel. Moreover, catalyst with several reactor concepts like the fluidized bed and membrane reactor were investigated by CFD simulation, process simulation and experiments, in order to study catalyst life time, operation conditions and technical feasibility. Thus, the reaction section was improved from 16% yield to 18%. Furthermore, the separation part of the OCM process was energetically improved by an integrated down streaming unit for the CO2. Thus, an energetic improvement of more than 40% in comparison to a benchmark absorption - desorption based CO2 separation process was achieved. In addition to this, novel absorbents were studied starting with molecular simulation up to process simulation and experimental validation for the CO2 separation. The results of the integrated process development and optimization process for the OCM will be presented and an overview of the multi scale and multilevel Process System Engineering (PSE) approach will be given for the case study.
Barz T, Löffler V, Arellano-Garcia H, Wozny G (2010) Optimal determination of steric mass action model parameters for beta-lactoglobulin using static batch experiments., Journal of chromatography. A 1217 (26) pp. 4267-4277
In this work, parameters of the steric mass-formalism SMA are optimally ascertained for a reliable determination of the adsorption isotherms of beta-lactoglobulin A and B under non-isocratic conditions. For this purpose, static batch experiments are used in contrast to the protocols based on different experimental steps, which use a chromatographic column. It is shown that parameters can already be determined for a small number of experiments by using a systematic procedure based on optimal model-based experimental design and an efficient NLP-solver. The in different works observed anti-Langmuir shape of the isotherm for small concentrations of beta-lactoglobulin A was corroborated. Moreover, we also found indications for a porosity variation with changing protein concentrations.
A novel approach for the systematic and hierarchical derivation of process models is presented. Model candidates for different unit phenomena are collected and rated on the basis of the model structure, origin and the modeler's belief. The process model is created as a superstructure with the competing partial models. Thereby, it is possible to determine the best possible combination through optimization with respect to different objective functions. The systematic procedure has been implemented into the online web modeling platform MOSAIC. Based on the superstructure, optimization code for the state-of-the art optimization and simulation software can automatically be created. Based on two case studies, the new approach is demonstrated, namely a process model for the hydroformylation of long-chain olefins and a model for the pressure drop in packed columns with foaming components.
Synthesis gas (syngas), mainly constituted by carbon monoxide (CO) and hydrogen gas (H2), is produced mostly through biomass gasification and methane reforming. In the last decade, the thermochemical route to produce ethanol and higher alcohols from syngas has been gaining space as a possible route to produce synthetic fuels and additives. This kind of process presents a series of advantages as: short-time reaction, abundant and lower-price feedstocks, the use of lignin and the almost complete conversion of syngas, having the potential to exceed ethanol production by fermentative route. Aiming to produce ethanol through thermochemical route, a singular process (a small-scale plant with capacity to process 100 kmol/h of syngas) was proposed for a first evaluation using the commercial simulator ASPEN Plus v7.3. Four different Rh-based catalysts were tested in the process (RhFe, RhLa, RhLaV, and RhLaFeV), trying to take advantage of the characteristics of Rh-based catalysts as high ethanol selectivity and hydrocarbons production. The process design took into account the reactor selectivity and conversion. Through sensitivity analysis, the downstream process were configured searching for the best possible design of separation steps, making possible to obtain ethanol (>99 % wt.), methanol (>90 % wt.), Liquified Petroleum Gas (LPG, mixture of C2H6, C3H8 and C4H10, > 99 % wt.) and pentane (>95% wt.).
Arellano-Garcia H, Carmona I, Wozny G (2008) A new operation mode for reactive batch distillation in middle-vessel columns: Start-up and operation., Computers & Chemical Engineering 32 1-2 pp. 161-169
Cruz Bournazou MN, Arellano-Garcia H, Wozny G, Lyberatos G, Caravaris C (2012) ASM3 extended for two-step nitrification?denitrification: a model reduction for sequencing batch reactors, Journal of Chemical Technology and Biotechnology 87 (7) pp. 887-896 Wiley
BACKGROUND: The ASM3 extended for two-step nitrification?denitrification represents the most accurate model for the description of the activated sludge process with nitrate bypass nitrification?denitrification. This model includes 20 reaction rates, 15 state variables, and more than 35 parameters, which make its calculation costly and difficult to estimate. The lack of a fast and accurate model able to predict both concentration of nitrite and nitrate over time is the principal obstacle for efficient model-based optimization and model-based control.
RESULTS: In this work, a fast and accurate model for the activated sludge process in a sequencing batch reactor is proposed. For this purpose, the ASM3 extended for two-step nitrification?denitrification, a 15-state variable model built for a general description of the ASP, is reduced to match the specific conditions of sequencing batch reactor systems with shortcut biological nitrogen removal to a nine-state model and then further to a six-state and five-state model under appropriate assumptions. The proposed model maintains the two-step nitrification?denitrification process feature of the original model and can thus describe the bypass of nitrate, showing increased tractability and lower computer costs. Different approaches for model reduction together with an exhaustive analysis of the extended ASM3 model as well as the process are discussed.
CONCLUSIONS: The resulting model with only five differential equations reduces the calculation time by up to one order of magnitude, while maintaining a high description accuracy, demonstrating the advantages of model reduction.
An important aspect for model-based design and development as well as for process monitoring and control is the consideration of uncertain process parameters. One approach for the explicit consideration of such uncertainties is the formulation of Chance-Constrained optimization problems. Within the last years, several different methods for the efficient solution of these problems have been presented. In this work, chance constraints are evaluated following the idea of the variable mapping approach.
Because the efficiency of the original approach deteriorates with an increasing number of uncertain parameters, the probability integration has been extended recently to the exploitation of sparse grids. In this work, additional techniques for improving the efficiency of the variable mapping approach are presented. Firstly, the solution of a subproblem, the so called shooting task is analyzed in detail and enhanced through an idea called here result recycling. Secondly, possible extensions are presented which make use of second order derivative information. The new methods are verified by application to an industrially validated process model of a vacuum distillation column for the separation of multicomponent fatty acids.
In this work, a new approach to model identification based on model-based experimental design is presented. In the proposed strategy, system identification relies on a closed-loop set-point response. For this purpose, experiments are first exemplarily executed with a P-controller. Therefore, in this specific case only one design variable is considered that is represented by the controller gain. In order to validate our approach and demonstrate the benefits of the proposed strategy different scenarios are simulated.
Hydrogen produced by microalgae is intensively researched as a potential alternative to conventional energy sources. Scaling-up of the process is still an open issue, and to this end, accurate dynamic modeling is very important. A challenge in the development of these highly nonlinear dynamic models is the estimation of the associated kinetic parameters. This work presents the estimation of the parameters of a revised Droop model for biohydrogen production by Cyanothece sp. ATCC 51142 in batch and fed-batch reactors. The latter reactor type results in an optimal control problem in which the influent concentration of nitrate is optimized which has never been considered previously. The kinetic model developed is demonstrated to predict experimental data to a high degree of accuracy. A key contribution of this work is the prediction that hydrogen productivity can achieve 3365 mL/L through an optimally controlled fed-batch process, corresponding to an increase of 116% over other recently published strategies.
This paper, inspired by the success of adsorptive air separation in big scale (up to 250 tons/day), looks into the possibility of replacing cryogenic distillation with adsorptive separation, and thus improving the downstream processing of OCM. This results in a new process concept. For this purpose, a plug flow model of fixed-bed adsorber was developed and several separation schemes were investigated via simulation. Among them, the simultaneously separation of ethylene and carbon dioxide using zeolite 4A is found realizable. The results show that by switching from cryogenic distillation to adsorption, separation cost can be significantly reduced.
The oxidation of sulfur dioxide over vanadium pentoxide catalysts represents a basic step in the sulfuric acid production process. In conventional sulfuric acid plants the SO2 oxidation represents the limiting step with respect to the SO2 emissions. Due to the fact that the SO2 oxidation is an equilibrium reaction, sulfuric acid plants always have SO2 emissions. In this work, a new process concept is presented, which uses the transient behaviour of the reaction in two reactors operating under unsteady conditions (Saturated Metal Phase reactor). Besides several advantages, which can increase the efficiency of the whole sulfuric acid process drastically, the SMP Reactor is a key component for an efficient operation of a sulfuric acid plant which reduces the emissions down to zero while keeping the necessary conditions for the hydrogenation unit installed downstream. For this purpose, a mathematical model is used, which describes the dynamic effects of the SO2 oxidation. The model has been experimentally verified in a Miniplant, which works with commercial catalyst pellets.
Esche E, Arellano-Garcia H, Biegler LT, Wozny G (2012) Two-dimensional modeling of a packed-bed membrane reactor for the oxidative coupling of methane, Chemical Engineering Transactions 29 (August) pp. 1537-1542 The Italian Association of Chemical Engineering
Oxidative coupling of methane (OCM) represents an opportunity for the replacement of crude oil, which still is the main source for longer hydrocarbons and almost all base chemicals, with natural gas, or biogas. OCM turns methane catalytically into mostly ethylene and ethane. Thus, several different reactor types exist, out of which the packed-bed membrane reactor (PBMR) is one of the most promising given its combination of reaction and product separation in one apparatus and also the improved temperature control because of the gradual feeding of oxygen through the membrane.
In previous simulation and optimization studies, one-dimensional models have been used to describe the conventional PBMR. However, due to radial diffusion and thermal conduction those models are not accurate enough. In this work, a two-dimensional model for the CPBMR is presented. Radial diffusion and thermal conduction in the packed-bed as well as in the reactor shell are considered while axial dispersion is neglected. In accordance with experimental studies, Knudsen?s diffusivity theory is applied to describe the flux through the membrane. The model is discretized using a combination of Lagrangian and Hermite collocating polynomials on finite elements. The two-dimensional model contains second order derivatives for the radial coordinate. Hence, continuity of both the collocated variable and the first derivative across all finite elements are required in that direction. In this case, Hermite polynomials are advantageous because they allow for the afore-mentioned continuity while negating the necessity of additional equality constraints.
As an initial configuration, a length of 20 cm is assumed for the CPBMR with two separate heating/cooling segments of each 10 cm. The tube-side and shell-side diameters are set to 7 and 10 mm, respectively. Preliminary studies have shown that five radial and twelve axial finite elements are required to ensure a stable performance of all optimization studies for the given initial configuration. The resulting large-scale NLP contains more than 130,000 variables. Most fluid properties and transport parameters are implemented as functions of local temperatures and concentrations rather than average values. A brief study shows that their joint influence cannot be neglected. Using La2O3/CaO as a catalyst with kinetics provided by Stansch et al. (1997), an overestimation of more than 25 percentage points can be observed in the yield of C2 hydrocarbons in a one
For more than three decades Oxidative Coupling of Methane (OCM) process has been investigated as an attractive alternative for cracking technologies for ethylene production and exploiting the huge resources of natural gas. Developing a uitable catalyst and analyzing proper reactor feeding policy, reviewing and deploying the efficient methods in separation and purification of the undesired and desired products, possible energy saving and process intensification in each section, each has been the subject of many researches in the past. In this paper, these aspects will be addressed simultaneously in a general overview of the main research activities performed in the chair of process dynamics and operation at Berlin Institute of Technology under the context of Unifying Concepts in Catalysis (UniCat) project. Moreover, a cost estimation of the industrial scale OCM process guiding the analysis method to address the potentials and disadvantageous of each OCM scenario structure, highlighted the possible process intensifications potentials in case of energy and equipment.
Hoang MD, Barz T, Merchan V, Biegler LT, Arellano-Garcia H (2013) Simultaneous solution approach to model-based experimental design, AIChE Journal 59 (11) pp. 4006-4451 American Institute of Chemical Engineers
A model-based experimental design is formulated and solved as a large-scale NLP problem. The key idea of the proposed approach is the extension of model equations with sensitivity equations forming an extended sensitivities-state equation system. The resulting system is then totally discretized and simultaneously solved as constraints of the NLP problem. Thereby, higher derivatives of the parameter sensitivities with respect to the control variables are directly calculated and exact. This is an advantage in comparison with proposed sequential approaches to model-based experimental design so far, where these derivatives have to be additionally integrated throughout the optimization steps. This can end up in a high-computational load especially for systems with many control variables. Furthermore, an advanced sampling strategy is proposed which combines the strength of the optimal sampling design and the variation of the collocation element lengths while fully using the entire optimization space of the simultaneous formulation.
This work presents the mathematical formulation of a nonlinear programming (NLP) model which optimizes simultaneously crude oil blending and operating conditions for a system of several crude oil distillation units (CDUs) at a Colombian refinery. The CDU system consists of three industrial units processing a blending of five extra-heavy crude oils and producing two commercial fuels, Jet-1A and Diesel. The NLP model involves typical restrictions (e.g., flow rate according to capacity of pumps, distillation columns, etc.) and the heat integration of streams from atmospheric distillation towers (ADTs) and vacuum distillation towers (VDTs) with the heat exchanger networks for crude oil preheating. A metamodeling approach is used so as to represent the ADTs. Preheating networks are modeled with mass, energy balances, and design equations of each heat exchanger. The NLP model has been implemented in GAMS using CONOPT as solver. Different cases are solved by the NLP model such that the optimal case with less profit increment had an economical benefit of 13% with respect to its case without optimization. In each optimal case the extra-heavy crude oils in the feed blending of each CDU required more severe operating conditions such as higher temperature of the crude oil at the entrance to the towers, greater flow rate of stripping steam at the bottom, and minor pressure of the tower tops.
This paper evidences the viability of chemical recycling of CO2 via reverse water-gas shift reaction using advanced heterogeneous catalysts. In particular, we have developed a multicomponent Fe-Cu-Cs/Al2O3 catalyst able to reach high levels of CO2 conversions and complete selectivity to CO at various reaction conditions (temperature and space velocities). In addition, to the excellent activity, the novel-Cs doped catalyst is fairly stable for continuous operation which suggests its viability for deeper studies in the reverse water-gas shift reaction. The catalytic activity and selectivity of this new material have been carefully compared to that of Fe/Al2O3, Fe-Cu/Al2O3 and Fe-Cs/Al2O3 in order to understand each active component?s contribution to the catalyst?s performance. This comparison provides some clues to explain the superiority of the multicomponent Fe-Cu-Cs/Al2O3 catalyst
Ethylene is the world?s largest commodity chemical and a fundamental building block molecule in the chemical industry. Oxidative coupling of methane (OCM) is considered a promising route to obtain ethylene due to the potential of natural gas as a relatively economical feedstock. In a recent work, this route has been integrated by Godini et al (2013) with methane dry reforming (DRM) in a dual membrane reactor, allowing an improved thermal performance.
In this work, we have explored a more ambitious integrated system by coupling the production of methane and carbon dioxide via coal gasification with the DRMOCM unit. Briefly, our process utilises coal to generate value-added methane and ethylene. In addition, CO2 management is achieved through CO2 methanation and dry methane reforming. Potential mass and energy integration between two systems is proposed as well as the optimum conditions for synthetic natural gas production. The upstream gasification process is modelled to determine the influence of temperature, pressure, and feed composition in the methane yield. The results suggest that the key variables are temperature and hydrogen concentration, as both parameters significantly affect the methane and CO2 levels in the linking stream. This study reports for the first time the linking stream between the two systems with a high methane concentration and the appropriate amount of CO2 for downstream processing.
In this work, the systematic integration of bio-refineries within oil refineries is considered. This is particularly relevant due to the lack of adaptation of existing refineries to diminishing oil supply. Moreover, the integration of oil and bio-refineries has a massively positive effect on the reduction of CO2 emissions. For instance, the biodiesel produced in bio-refineries could be integrated with conventional oil refinery processes to produce fuel, thusly reducing the dependence on crude oil. This represents a suitable alternative for increasing profit margins while being increasingly environmentally friendly. The identified possible routes of integration will be discussed in this contribution. For this purpose, the different proposed alternatives and their configurations were simulated and analysed. The developed models simulated key integrations e.g. a gasification unit that is fed from pyrolysis oil, biodiesel, and refinery residue, before being combined into one system involving all three. Varying forms of synthesis for these three feeds were also considered, focusing on novel techniques as well as environmentally friendly options that made use of waste products from other processes. The simulations revealed valuable gas stream rich in H2, with some CO2 and with a slight excess of CO resulting from the gasification unit. Further upgrading of these products was achieved by coupling the gasifier with a water gas shift (WGS) unit. This allowed a fine tune of the H2:CO ratio in the gas stream which can be further processed to obtain liquid hydrocarbons via Fischer-Tropsch (FT) synthesis or alternatively, clean hydrogen for fuel cells applications.
We investigate homogeneous nuclear matter within the Brueckner-Hartree-Fock (BHF) approach in the limits of isospin-symmetric nuclear matter (SNM) as well as pure neutron matter at zero temperature. The study is based on realistic representations of the internucleon interaction as given by Argonne v18, Paris, Nijmegen I and II potentials, in addition to chiral N3LO interactions, including three-nucleon forces up to N2LO. Particular attention is paid to the presence of di-nucleon bound states structures in 1S0 and 3SD1 channels, whose explicit account becomes crucial for the stability of self-consistent solutions at low densities. A characterization of these solutions and associated bound states is discussed. We confirm that coexisting BHF single-particle solutions in SNM, at Fermi momenta in the range 0.13 ? 0.3 fm?1 , is a robust feature under the choice of realistic internucleon potentials.
The objective of this study is to demonstrate that the accurate mathematical description of phosphate dynamics requires a considerable, but unavoidable, degree of complexity when modelling biotechnological systems. As an example, a model predicting antibiotic production using Streptomyces coelicolor is chosen which had difficulties explaining the phosphate dynamics. The model is enhanced by the implementation of an advanced speciation model and a multiple mineral precipitation framework. Furthermore, a model describing intracellular polyphosphate accumulation and consumption is developed and implemented. Based on the conducted work the improved process model is capable of predicting the phosphate dynamics (RMSEd 52h: -90 %, RADd 52h: -96 %) very accurately in comparison to the original implementation, where biomass growth was the only phosphate sink. The description of most other variables was improved by a knowledge-based re-estimation of selected parameters as well. This work contributes to the existing process knowledge of biotechnological systems in general and especially to the antibiotic production with S. coelicolor, which emphasizes the necessity of combining physico-chemical and biological processes to accurately describe phosphate dynamics.
Despite having very clean combustion properties, the majority of hydrogen produced today still comes from fossil fuels. As such, there is a demand for renewably produced hydrogen, such as solar powered electrolysis, so that the hydrogen produced retains its clean credentials. Unfortunately, this process is plagued by inefficiencies and requires improvement in order to economically compete with fossil fuels. This work investigates solar hydrogen production via aqueous methanol (MeOH) electrolysis in comparison to pure water electrolysis in a directly coupled solar-PEM electrolysis system. Experiments were completed to investigate the impact of changing the MeOH concentration, power supply, and load characteristics on electrolysis and solar-hydrogen efficiencies. Simulation studies were then performed to analyse thoroughly the experimental data so as to gain an understanding of the yields and economics of utility scale solar?hydrogen facilities.
Catalytic hydrotreating is one of the processes used intensively in the modern petroleum refining industry. It is series of reactions considered as a mature process that improves the quality of petroleum products and removes Sulphur and undesired impurities. This study aims to develop and enhance the performance of a whole petroleum refining plant, which follows the concept of crude oil hydrotreating (HDT). The study was carried out using Aspen HYSYS simulator building a model-based analysis for the refinery plant. Two refineries have been simulated separately; one with a crude oil hydrotreating and the other followed the conventional method. The comparison and analysis focused on enhancing the yield of middle distillates while reducing the total energy consumption and overall costs. Hydrodenitrogenation and Hydrodesulfurization were the two reactions that took place in the trickle bed reactor at 400 °C and 10 MPa. The hydrotreated crude oil enters then the atmospheric distillation column, where six main products were distilled (LPG, Light Naphtha, Heavy Naphtha, Kerosene and Residual crude). In the model-based analysis, the crude HDT process configuration was completed first using Kirkuk crude oil, and to confirm the significance of the study, Siberian crude was used as an alternative feedstock. Finally, the results confirmed that the crude oil hydrotreating method can be followed using different types of feedstock around the world.
Novel renewable energy sources are necessary to counter the current environmental crisis. The largest source of renewable energy is the sun. One possible application of solar energy is the harvesting and storage of low temperature thermal heat (
The new models were compared with experimental data from two different test sites, concerning mainly the temperature at the lower convective zone (LCZ) and the upper convective zone (UCZ). The 3D model was proven to be the most accurate with the 1D model being the least. Furthermore, the general radiative heat transfer equation, with an isotropic scattering phase function, solved using the discrete ordinates method was proven to give a satisfactory accuracy in terms of radiation in semi-transparent media.
Carbon formation and sintering remain the main culprits regarding catalyst deactivation in the dry and bi-reforming of methane reactions (DRM and BRM, respectively). Nickel based catalysts (10 wt.%) supported on alumina (Al2O3) have shown no exception in this study, but can be improved by the addition of tin and ceria. The effect of two different Sn loadings on this base have been examined for the DRM reaction over 20 h, before selecting the most appropriate Sn/Ni ratio and promoting the alumina base with 20 wt.% of CeO2. This catalyst then underwent activity measurements over a range of temperatures and space velocities, before undergoing experimentation in BRM. It not only showed good levels of conversions for DRM, but exhibited stable conversions towards BRM, reaching an equilibrium H2/CO product ratio in the process. In fact, this work reveals how multicomponent Ni catalysts can be effectively utilised to produce flexible syngas streams from CO2/CH4 mixtures as an efficient route for CO2 utilisation.
Methods of systematic control structure design are adapted to suit industrial workflow of plant design and engineering in the chemical and process industry. The applied methods include a systematic step-wise design methodology and the concept of self-optimizing control. Implementation of these methods is performed in a commercial software framework that consists of Aspen Plus and MatLab. The control structure design methodology of an industrial sequence of heat-integrated distillation columns is presented as a case study.
Herein, the production of synthetic natural gas is proposed as an effective route for CO2 conversion. Typical catalysts for this reaction are based on Ni. In this study, we demonstrated that the addition of promoters such as iron and cobalt can greatly benefit the activity of standard Ni methanation catalysts. In particular cobalt seems to be a very efficient promoter. Our Co doped material is an outstanding catalysts for the CO2 methanation leading to high levels of CO2 conversion with selectivities close to 100%. Additionally, this catalyst is able to preserve excellent performance at relatively high space velocity which allows flexibility in the reactor design making easier the development of compact CO2 utilisation units. As an additional advantage, the Co-promoted catalysts is exceptionally stable conserving high levels of CO2 conversion under continuous operations in long terms runs.
The morphology of gravity-driven rivulets affects the mass transfer performance
in gas separation processes, hence, the need for an improved knowledge on the
hydrodynamics of this
ow. It is well established that the interface area of
the rivulets is determined by the balance between inertia and surface tension,
i.e. the Weber number, which in light of the results presented here, are not
the only parameters involved, but also the inclination of the plate has an effect
on the balance of forces which determines the amount of gas-liquid interface
area. The analysis of the interface area in rivulet
ow demands, therefore, a
more complete physical explanation for packing design purposes. In this work,
we analyse the combined effect of both the inertia and the inclination of the
plate in the interface area of liquid rivulets using CFD and the Volume-of-Fluid
interface tracking method. As a result, we propose the use of the Froude number
to provide a more complete physical explanation on the interface area formation
of gravity-driven liquid rivulets.
In recent years, the production of waste materials because of population growth is increasing. Accordingly, finding new methods to convert such materials into fuel are becoming more popular. The objective of this study is to determine the potential of dried distiller`s grains and soluble (DDGS), a by-product of bioethanol industry, to produce butanol using strains of Clostridium saccharoperbutylacetonicum (C.SCC). In addition, this research is intended to develop the fermentation kinetic by improving the bacterial growth and medium optimisation while enhancing the process parameters based on the experimental data. Therefore, to find the most optimum condition for DDGS as the growth medium, the crucial effect of pH and various DDGS media supplementation were identified. Consequently, after performing several experiments in batch serum bottles without agitation, it had been determined that the best condition for DDGS as the growth medium is supplementing the autoclaved DDGS medium with 10 % (v/v) sucrose solution while pH is adjusted to 6.5. Thus, when the supplemented DDGS medium was inoculated with 10 % (v/v) of Clostridium saccharoperbutylacetonicum, the butanol concentration improved to approximately 7.2 (g/L) while this amount for the non-supplemented DDGS medium was approximately 4.4 (g/L). However, under the same conditions for DDGS as the growth medium, the butanol concentration decrease to 0.22 (g/L) while agitation was involved in the batch bioreactor. Based on the experimental results obtained from various experiments of this research, it is concluded that Clostridium saccharoperbutylacetonicum microorganism can ferment sucrose and other carbon sources available in DDGS such as glucose. Moreover, to improve the butanol concentration using mathematical modeling and computer simulation in ongoing studies, the collected preliminary data of this experimental research could be used in the proposed kinetic model by Shinto et al. (2007).
The dry reforming of methane with CO2 is a common route to transform CO2/CH4 mixtures into added value syngas. Ni based catalysts are highly active for this goal but suffer from deactivation, as such promoters need to be introduced to counteract this, and improve performance. In this study, mono- and bi-metallic formulations based on 10 wt.% Ni/CeO2-Al2O3 are explored and compared to a reference 10 wt.% Ni/³-Al2O3. The effect of Sn and Pt as promoters of Ni/CeO2-Al2O3 was also investigated. The formulation promoted with Sn looked especially promising, showing CO2 conversions stabilising at 65% after highs of 95%. Its increased performance is attributed to the additional dispersion Sn promotion causes. Changes in the reaction conditions (space velocity and temperature) cement this idea, with the Ni-Sn/CeAl material performing superiorly to the mono-metallic material, showing less deactivation. However, in the long run it is noted that the mono-metallic Ni/CeAl performs better. As such the application is key when deciding which catalyst to employ in the dry reforming process.
The multiplicity of physico-chemical and biological processes, where phosphorus is involved, makes their accurate prediction using current mathematical models in biotechnology quite a challenge. In this work, an antibiotic production model of Streptomyces coelicolor is chosen as a representative case study in which major difficulties arise in explaining the measured phosphate dynamics among some minor additional issues. Thus, the utilization of an advanced speciation model and a multiple mineral precipitation framework is proposed to improve phosphorus predictions. Furthermore, a kinetic approach describing intracellular polyphosphate accumulation and consumption has been developed and implemented. A heuristic re-estimation of selected parameters is carried out to improve overall model performance. The improved process model predicts phosphate dynamics (Root Mean Squared Error d52h: -90 %, Relative Average Deviation d52h: -96 %) very accurately in comparison to the original implementation, where biomass growth/decay was the only phosphorus source-sink. In addition, parameter re-estimation achieved an improved description of the available measurements for biomass, total ammonia, dissolved oxygen and actinorhodin concentrations.
This work contributes to the existing process knowledge of biotechnological systems in general and especially to antibiotic production with S. coelicolor, while emphasizing the (unavoidable) need of considering both physico-chemical and biological processes to accurately describe phosphorus dynamics.
The UK is committed to reducing its greenhouse gas emissions by at least 80% by 2050, relative to 1990 levels. For this to happen, we need to transform the UK economy while ensuring secure, low-carbon energy supplies to 2050. The future electricity distribution system, known as smart grid, will integrate advanced digital meters, distribution automation, communication systems and distributed energy resources. There has been a lot of discussion about the importance of the Internet of Things (IoT) in future smart grids and smart cities stating that IoT offers many applications and can be used to integrate efficiency renewable energy sources in the smart grid by making the electricity grid more robust and scalable.
This study will focus on the development of an integrated IoT-Distributed energy systems (DES) model for the efficient energy management of a microgrid under the integration of the intermittent renewable energy resources. In this work, we expand the definition of flexible options to include demand and supply together with design and operation strategies using internet of things (IoT). Our framework brings weather data and sensor information into a virtual energy plant optimisation model that connects supplier and consumer to optimise potential flexibility gaps arising from supply and demand mismatch. The problem is posed as a hybrid mixed-integer linear programming (MILP) optimisation model combining flexibility analysis and optimal synthesis for integrating energy supply and demand, where environmental information is added to each stage. Finally, we combine traditional mathematical programming approaches such as flexibility analysis and optimal network synthesis and within a single optimisation framework combining IoT and urban DES.
Dorneanu Bogdan, Mechleri Evgenia, Arellano-Garcia Harvey (2018) Towards the cooperative-based control of chemical plants, In: Friedl Anton, Klemea Jiri J, Radl S, Varbanov Petar S, Wallek Thomas (eds.), Computer Aided Chemical Engineering (Part of volume: European Symposium on Computer Aided Process Engineering) 43 pp. 1087-1092
This contribution presents the proof of concept for a consensus-based approach for the design and assessment of control structures in chemical plants. The applicability of the proposed approach is demonstrated on an existing mini-plant. For this purpose, a reduced dynamic model that considers a simplified structure of the plant, consisting of feed preparation, reaction, and downstream processing, is used to assess the control structure of the mini-plant using the consensus algorithm. The reduced model is used to assess the control structure of the mini-plant reactor, considering the relevant operational and safety variables.
An optimised integration approach connecting a conventional oil refinery with an ethylene production plant is investigated. Using the intermediate materials produced as the connection between the two plants, the use of internally provided feedstocks and blending options removes, at least partially, the reliance on external sourcing. This is also beneficial in terms of increasing profit margins and quality for both production systems. Thus, a mathematical model has been developed and implemented in this work to model the oil refinery and the ethylene production plant while considering their integration as an MINLP problem with the aim of optimising the integrated plants. This work considers the optimisation of each plant individually and later the final integration by modelling the interconnection between the oil refinery and the ethylene production plant. Moreover, a case study using practical data was carried out to verify the feasibility of the integration for an industrial application.
Fractals are the evolutionary answer that Nature has developed to provide highly packed structures for mass and heat transfer. In this work, a computational fluid dynamics model will be presented to gain insight on the multiphase flow characteristics within fractal geometries. A substantial increment of the gas-liquid interface area with the fractal dimension of the particular geometry being tested is expected. This will allow the implementation of more compact designs, i.e. with greater specific area, than the conventional structured packings currently used for gas separation. More compact designs also mean less solvent used for the same gas absorption rate, reducing the heat duty of solvent regeneration. The next steps of this research will include the use of 3D printing techniques to reproduce fractal geometries to be tested in an experimental setup, and the study of the interaction between nature-inspired gas absorbers and the rest of the carbon emitting facility through process simulations.
Modern renewable energy sources have a great disadvantage of being intermittent. Harvesting solar energy directly using photovoltaic panels is one of the most promising renewable energy technologies. While this allows electricity generation during daytime when the sky is clear, at night there is no production at all and it is greatly diminished in cloudy or rainy conditions. Recently a concept of all-weather solar cells was proposed by Q. Tang et al. (Angew. Chem. Int. Ed. 55(17) (2016) 5243-5246) in which a solar panel was covered with a layer of graphene. This allows collecting energy from falling raindrops containing dissolved salts through charging and discharging of an electrical double layer at the water-graphene interface, which acts as a pseudocapacitor. Although this setup allows harvesting both direct solar radiation and some of the kinetic energy of falling rain drops, the output is low for realistic salt concentrations while the graphene layer diminishes the solar-to-electric conversion rate.
In this work, we propose a different approach to the same problem. Instead of relying on a sufficient concentration of salts in rain water, we propose to convert the mechanical energy delivered by drop impacts directly into electrical energy by supporting a thin-layer solar panel with an array of piezo crystals. The advantage of this setup is that the solar-to-electric performance of such a panel is not affected by the added piezoelectric support. However, only a fraction of the kinetic energy of the falling rain drops can be converted due to the energy dissipation within the material of the thin-layer panel. We have conducted detailed modelling of kinetic energy harvesting process from the drop impact and spreading to the dissipation of mechanical strain through the panel to the generation of piezoelectric potential. The results illustrate the viability of this concept, but they are still to be confirmed experimentally and require an economic feasibility analysis to be performed.
Spray drying is a basic unit operation in several process industries such as food, pharmaceutical, ceramic, and others. In this work, a Eulerian-Lagrangian three-phase simulation is presented to study the drying process of barbotine slurry droplets for the production of ceramic tiles. To this end, the simulated velocity field produced by a spray nozzle located at the Institute of Ceramic Technology in Castelló (Spain) is benchmarked against measurements obtained by means of laser Doppler anemometry in order to validate the numerical model. Also, the droplet size distribution generated by the nozzle is obtained at operating conditions by means of laser diffraction and the data obtained are compared qualitatively to those found in the literature. The droplet size distribution is introduced thereafter in the three-phase simulation to analyse the drying kinetics of individual droplets. The model predicts the theoretical linear evolution of the square diameter (D2-law), and the temperature and mass exchange with the environment. The proposed model is intended to support the design and optimization of industrial spray dryers.
This contribution introduces a framework for the fault detection and healing of chemical processes over wireless sensor networks. The approach considers the development of a hybrid system which consists of a fault detection method based on machine learning, a wireless communication model and an ontology-based multi-agent system with a cooperative control for the process monitoring.