A Modeling Approach to Reaction of Non-Newtonian Fluids in Tubular Reactor

The treatment of non-Newtonian fluids in tubular reactors is frequently encountered in industries, which may be studied by mathematical modeling. The modeling of endothermic reactions of non-Newtonian fluids in tubular reactors with radial dispersion has been done for Ostwald-de-Waele power law fluids. The coupled mass balance, heat balance and velocity equations have been dealt with. The model is solved using finite difference numerical methods. The effect of variation in the dimensionless parameters of the model has also been studied. In addition, the rheological parameter n also affects the reactor performance as well as the velocity profile. An increase in n leads to higher velocity distortion and a decrease in conversion and temperature.     

Neural network model-based predictive control of liquid-liquid extraction contactors

The inherent complex nonlinear dynamic characteristics and time varying transients of the liquid-liquid extraction process draw the attention to the application of nonlinear control techniques. In this work, neural network-based control algorithms were applied to control the product compositions of a Scheibel agitated extractor of type I. Model predictive control algorithm was implemented to control the extractor. The extractor hydrodynamics and mass transfer behavior were modeled using the non-equilibrium backflow mixing cell model. It was found that model predictive control is capable of solving the servo control problem efficiently with minimum controller moves. This study will be followed by more work concentrated on using different neural network-based control algorithms for the control of extraction contactors.     

Optimal hybrid modeling approach for polymerization reactors using parameter estimation techniques

The dynamics of polymerization catalytic reactors have been investigated by many researchers during the past five decades; however, the emphasis of these studies was directed towards correlating process model parameters using empirical investigation based on small scale experimental setup and not on real process conditions. The resulting correlations are of limited practical use for industrial scale operations. A statistical study for the relative correlation of each of the effective process parameters revealed the best combination of parameters that could be used for optimizing the process model performance. Parameter estimation techniques are then utilized to find the values of these parameters that minimize a predefined objective function. Published real industrial scale data for the process was used as a basis for validating the process model. To generalize the model, an artificial neural network approach is used to capture the functional relationship of the selected parameters with the process operating conditions. The developed ANN-based correlation was used in a conventional fluidized catalytic bed reactor (FCR) model and simulated under industrial operating conditions. The new hybrid model predictions of the melt-flow index and the emulsion temperature were compared to industrial measurements as well as published models. The predictive quality of the hybrid model was superior to other models. The suggested parameter estimation and modeling approach can be used for process analysis and possible control system design and optimization investigations.     

MODELING OF ETHANOL BIOPRODUCTION IN THREE-PHASE FLUIDIZED BED REACTORS

A time dependent and one-dimensional model is developed to analyze the performance of three-phase fluidized reactors and is applied to the fermentation of glucose to ethanol. The reactor model takes into consideration the presence of three different phases; the yeast (solid) which is continuously fluidized by the liquid stream, the gas bubbles which greatly enhance mixing and the wake phase which follows the tracks of the gas bubbles. The reactor performance is analyzed as a function of major operating conditions. The analysis includes variations in dispersion of glucose and yeast inside the reactor, the concentration of glucose in feed, and of the yeast mass inside the reactor, reaction temperature, velocities of gas and liquid feeds, and reactor aspect ratio. Computed glucose conversion is presented as a function of reactor length and time. The results indicate that high glucose conversions can be obtained at high gas velocities, low liquid flow rates, large aspect ratios, high yeast concentration, and an optimum operating temperature of 36°C,     

Generalized delta rule (GDR) algorithm with generalized predictive control (GPC) for optimum temperature tracking of batch polymerization

The generalized delta rule (GDR) algorithm with generalized predictive control (GPC) control was implemented experimentally to track the temperature on a set point in a batch, jacketed polymerization reactor. An equation for optimal temperature was obtained by using co-state Hamiltonian and model equations. To track the calculated optimal temperature profiles, controller used should act smoothly and precisely as much as possible. Experimental application was achieved to obtain the desired comparison. In the design of this control system, the reactor filled with styrene-toluene mixture is considered as a heat exchanger. When the reactor is heated by means of an immersed heater, cooling water is passed through the reactor-cooling jacket. So the cooling water absorbs the heat given out by the heater. If this is taken into consideration, this reactor can be considered to be continuous in terms of energy. When such a mixing chamber was used as a polymer reactor with defined values of heat input and cooling flow rate, system can reach the steady-state condition. The heat released during the reaction was accepted as a disturbance for the heat exchanger. Heat input from the immersed heater is chosen as a manipulated variable. The neural network model based on the relation between the reactor temperature and heat input to the reactor is used. The performance results of GDR with GPC were compared with the results obtained by using nonlinear GPC with NARMAX model.The reactor temperature closely follows the optimal trajectory. And then molecular weight, experimental conversion and chain lengths are obtained for GDR with GPC.     

非均相光催化水处理管式反应器的放大设计

研究了非均相光催化反应器在放大过程中光辐射能衰减和流体返混对其放大过程设计的影响。结果表明,光辐射能随着光源和反应管的中心距的增加急剧衰减,两者呈1 56次方反比关系;管式反应器中流体的返混程度远小于相同反应体积的环型反应器,这有利于提高水中有机污染物的降解率。建立了一套由3组管式反应器串联且光接触面与光源的距离小于10cm的连续光催化水处理中试装置,并对印染废水进行处理。化学需氧量可从150～180mg/L降至50mg/L以下,处理量为50L/h时的出水优于国家一级排放标准。     

Downer reactor: From fundamental study to industrial application

Downer reactor, in which gas and solids move downward co-currently, has unique features such as the plug-flow reactor performance and relatively uniform flow structure compared to other gas-solids fluidized bed reactors, e.g., bubbling bed, turbulent bed and riser. Downer is therefore acknowledged as a novel multiphase flow reactor with great potential in high-severity operated processes, such as the high temperature, ultra-short contact time reactions with the intermediates as the desired products. Typical process developments in industry have directed to (1) the new-generation refinery process for cracking of heavier feedstock to gasoline and light olefins (e.g., propylene) as by-products; and (2) coal pyrolysis in hydrogen plasma which opens up a direct means for producing acetylene, i.e., a new route to synthesize chemicals from a clean coal utilization process. This paper is to give a comprehensive review on the development of fundamental researches on downer reactors as well as the particular industrial demonstrations for the fluid catalytic cracking (FCC) of heavy oils and coal pyrolysis in thermal plasma.     

Dynamics of a solar thermochemical reactor for steam-reforming of methane

A nonlinear dynamic model is developed for a steam/methane-reforming reactor that uses concentrated solar radiation as the source of high-temperature process heat. The model incorporates a set of lumped-parameter reservoirs for mass and energy. For each reservoir, the unsteady mass and energy conservation equations are formulated, which couple conduction, convection, and radiation heat transfer with the temperature dependent chemical conversion. Radiative exchange, the dominant heat transfer mode at above 800 K, is solved by a band-approximation Monte Carlo technique. The dynamic model is applied to predict the transient behavior of a 400 kW prototype solar reformer in operational modes of purging, thermal testing, startup, chemical reaction, shutdown, and cyclical operation. Time constants vary between 2 s for species transport and for thermal energy transport through ceramic insulation. Validation is accomplished by comparing modeled and experimentally measured outlet gas temperatures obtained from reactor tests in a solar tower facility.     

NEURAL NETWORK–BASED HEAT AND MASS TRANSFER COEFFICIENTS FOR THE HYBRID MODELING OF FLUIDIZED REACTORS

The complex flow patterns induced in fluidized bed catalytic reactors and the competing parameters affecting the mass and heat transfer characteristics make the design of such reactors a challenging task to accomplish. The models of such processes rely heavily on predictive empirical correlations for the mass and heat transfer coefficients. Unfortunately, published empirical-based correlations have the common shortcoming of low prediction efficiency compared with experimental data. In this work, an artificial neural network approach is used to capture the reactor characteristics in terms of heat and mass transfer based on published experimental data. The developed ANN-based heat and mass transfer coefficients relations were used in a conventional FCR model and simulated under industrial operating conditions. The hybrid model predictions of the melt-flow index and the emulsion temperature were compared to industrial measurements as well as published models. The predictive quality of the hybrid model was superior to other models. This modeling approach can be used as an alternative to conventional modeling methods.