Neural Network Models And Sensitivity Analysis For The Production Of Isopropyl Myristate In Semibatch Reactive Distillation

Isopropyl myristate (IPM) is an important chemical in the cosmetic and pharmaceutical industries. The IPM can be produced either through esterification or the transesterification process in semibatch reactive distillation (BRD). However, the latter process is not widely explored. The transesterification process in BRD can be represented by a mathematical model, however, this model will end with a large number of differential equations and be very expensive to solve and will also be time consuming. Hence, the empirical model such as the artificial neural network (ANN) model provides better solution as it can deal with highly nonlinear and complex structures. In this work, the production of industrial scaled IPM in BRD through the transesterification process is simulated using Aspen Plus and the simulation result achieved shows a comparable result as reported in the literature. The validated model is then used for sensitivity analysis to determine the relationship between the process input-output variables. The nonparametric test is used and the selected inputs are ranked according to their mean overall sensitivity. From the results, the reboiler duty, the initial mole of isopropanol, methyl mysistate, the reflux ratio, the feed flowrate and the temperature at stage 32 are considered as the input variables in the ANN model development to predict the bottom and distillate composition

Feed Forward Neural Network Model for Isopropyl Myristate Production in Industrial-scale Semi-batch Reactive Distillation Columns

The application of the artificial neural network (ANN) model in chemical industries has grown due to its ability to solve complex model and online application problems. Typically, the ANN model is good at predicting data within the training range but is limited when predicting extrapolated data. Thus, in this paper, selected optimum multiple-input multiple-output (MIMO) and multiple-input single-output (MISO) models are used to predict the bottom (xb) compositions of extrapolated data. The MIMO and MISO models both managed to predict the extrapolated data with MSE values of 0.0078 and 0.0063 and with R2 values of 0.9986 and 0.9975, respectively

A Model-Centric Framework for Advanced Operation of Crystallization Processes

Crystallization is the main physical separation process in many chemical industries. It is an old unit operation which can separate solids of high purity from liquids, and is widely applied in the production of food, pharmaceuticals, and fine chemicals. While industries in crystallization operation quite rely on rule-of-thumb techniques to fulfill their requirement, the move towards a scientific- and technological- based approach is becoming more important as it provides a mechanism for driving crystallization processes optimally and in more depth without increasing costs. Optimal operation of industrial crystallizers is a prerequisite these days for achieving the stringent requirements of the consumer-driven manufacturing. To achieve this, a generic and flexible model centric framework is developed for the advanced operation of crystallization processes. The framework deploys the modern software environment combined with the design of a state-of-the-art 1-L crystallization laboratory facility. The emphasis is on developing an economically and practically feasible implementation which can be applied for the optimal operation of various crystallization systems by pharmaceutical industries. The key developments in the framework have occurred in three broad categories: i. Modeling: Using an advanced modeling tool is intended for accurate representation of the behavior of the physical system. This is the cornerstone of any simulation, optimization or model-based control approach. ii. Monitoring: Applying a novel image-based technique for online characterization of the particulate processes. This is a promising method for direct tracking of particle size and size distribution with high adaptability for real-time application iii. Control: Proposing numerous model-based strategies for advanced control of the crystallization system. These strategies enable us to investigate the role of model complexity on real-time control design. Furthermore, the effect of model imperfections, process uncertainty and decision variables on optimal operation of the process can be evaluated. Overall, results from this work presents a robust platform for further research in the area of crystal engineering. Most of the developments described will pave the way for future set of activities being targeted towards extending and adapting advanced modeling, monitoring and control concepts for different crystallization processes

Crystallisation route map

A route map for the assessment of crystallisation processes is presented. A theoretical background on solubility, meta-stable zone width, nucleation and crystal growth kinetics is presented with practical examples. The concepts of crystallisation hydrodynamics and the application of population balances and computational fluid dynamics for modelling crystallisation processes and their scaling up are also covered

Generic Model Control (GMC) in Multistage Flash (MSF) Desalination

YesMultistage Flash Desalination (MSF) is currently facing an enormous challenge in cutting of the cost: within the last few years, the MSF experienced a gradual decline in investment compared to other techniques of desalting water and thus, a significant improvement is required to remain attractive for capital investors. Improved process control is a cost effective approach to energy conservation and increased process profitability. In this work, a dynamic model is presented using gPROMS model builder to optimize and control MSF process. The Proportional Integral Derivative Controller (PID) and Generic Model Control (GMC) are used successfully to control the Top Brine Temperature (TBT) and the Brine Level (BL) in the last stage at different times of the year. The objectives of this study are: firstly, to obtain optimum TBT and BL profiles for four different seasons throughout the year by minimizing the Total Seasonal Operating Cost (TSOC); secondly, to track the optimum TBT and BL profiles using PID and GMC controllers with and without the presence of constraints; thirdly, to examine how both types of controllers handle the disturbances which occur in the plant. The results are promising and show that GMC controller provides better performance over conventional PID controller to handle a nonlinear system