Band 17, Heft 2

In this work we propose a contribution for the optimization of the tricalcium phosphate synthesis by a double decomposition with a Ca/P ratio equal to 1.5, using Taguchi’s approach in the methodology of experiment design. We used a model involving four factors, namely the pH of the reaction, the concentration of Ca 2+ ions, the temperature and the reaction time. We reduced the number of factors by subtracting the temperature, because it varies randomly in its range of variation. So we have developed a much simpler and more robust model by a double optimization. It consists in finding a configuration of the retained factors to synthesize a product with a Ca/P equal to 1.5 with the minimum of dispersion. We propose a synthesis process insensitive to random variations in temperature in its field of experimental variation defined previously. The synthesis process of the tricalcium phosphate is robust and insensitive on the temperature in the range of variation that was analyzed. The resolution of the mathematical model proposes different ways for this synthesis by a factorial variation of the three remaining factors. The proposed mathematical model is linear and efficient with very satisfactory statistical indicators.

In this study, the simulation of the natural gas liquids (NGLs) of Siri Island unit was carried out using Aspen Hysys software. Energy and economic evaluation of these towers were evaluated with Aspen Energy Analyses and Aspen Economic Evaluation, respectively. The effect of various parameters such as the use of heat exchanger instead of a cooler, feed tray changes and the mixing of two feed streams together were investigated to increase the efficiency of hydrocarbons - (Ethane, Propane, Butane, and Pentane) separation, utility consumption and costs. The simulation results showed that the input feed stream from the 10-tray using - mixing of two input 207 and 208 streams, which improves the efficiency of hydrocarbons gas separation and utility consumption compared to other parameters.

In two-phase flows in which the Capillary number is low, errors in the computation of the surface tension force at the interface cause Front-Capturing methods such as Volume of Fluid (VOF) and Level-Set (LS) to develop interfacial spurious currents. To better solve low Capillary number flows, special treatment is required to reduce such spurious currents. Smoothing the phase indicator field to more accurately compute the curvature or adding interfacial artificial viscosity are techniques that can treat this problem. This study explores OpenFOAM, Fluent and StarCCM+ VOF solvers for the classical case of a static bubble/droplet immersed in a continuous aqueous phase, with the focus on the ability of these solvers to adequately reduce spurious currents. The results are expected to be helpful for practicing chemical engineers who use multiphase CFD solvers in their work.

The aim of this paper is to analyze the performance of the air gap membrane distillation (AGMD) process for the separation of HCl/Water mixture first by applying Taguchi optimization approach and second by developing an artificial neural network (ANN) model. The experimental data which are fed as input to the above approaches are collected from the fabricated AGMD lab-scale setup using poly-tetra-fluoro-ethylene membrane of 0.22 µm pore size. The process input variables considered are bulk feed temperature, feed flow rate, air gap thickness, cooling water temperature and cooing water flow rate and AGMD performance index is the total permeate flux. The optimum operating condition is found to be at feed temperature 50 °C, air gap thickness 7 mm, cooling water temperature 5 °C and feed flow rate 10 lpm. Analysis of variance test is carried out for both Taguchi and ANN models. Regression model has also been developed for the comparison between experimental and model predicted data. The developed ANN model has been found well fitted with experimental data having R 2 value of 0.998. Based on the calculated percentage of contribution of each input parameter on the AGMD permeate flux, it can be concluded that feed temperature and air gap thickness have highest weightage whereas feed flow rate and cooling water temperature have moderate effects. Predictive ability of the developed ANN model is further checked with 2D contour plot. The distinctive feature of the paper is the development of the Taguchi experimental design and ANN model and then consequently integration of both Taguchi and ANN has been carried out to optimized the developed ANN model parameters.

In this paper, a novel mathematical model that combines a membrane filtration model, component balances and reaction kinetics models for an intensified separation-reaction process in membrane reactor producing biofuels was developed. A unique feature is that the proposed model can capture the dynamics of membrane fouling as function of both reversible and irreversible fouling; which leads to cyclic behavior. Fouling leads to the decline of the reactor productivity. With an appropriate fouling-model, the operational strategy can be optimized. In the case study of biodiesel production, the developed model was validated with experimental data. The model was in good agreement with the data, where R-squared are 0.96 for the permeate flux and 0.95 for the biodiesel yield. From a further analysis, the efficiency of membrane reaction system in term of productivity can be significantly improved by changing the backwashing frequency under specific operating conditions. As the backwashing frequency increased eight times, the biodiesel yield increased to more than two to three times before the permeate flux dropped under a predetermined limit due to the increase of irreversible membrane fouling.

Design of a control structure in distillation columns involves selecting proper sets of manipulated and controlled variables (often including tray temperatures for inferential control of product compositions) and one-to-one pairing between the two sets. In this paper, various mathematical tools for achieving this goal are reviewed. First, traditional methods such as Singular Value Decomposition (SVD) and Relative Gain Array (RGA) that build upon a simplified steady-state or dynamic model of the column are explored. The role of optimization in systematizing the control design procedures is also investigated. Then, more recent inferential control techniques that rely on statistical methods such as Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), and other machine learning techniques such as Artificial Neural Networks (ANN) and Support Vector Machine Regression (SVMR) are discussed extensively. The discussions include newer distillation technologies with complex configurations such as dividing-wall columns. Finally, the use of process simulators in aiding the control structure design of distillation columns is surveyed.