Cellular manufacturing system design considering machines reliability and parts alternative process routings

Cell formation is an important problem in the design of cellular manufacturing systems (CMS). Most cell formation methods appeared in the literature assume that each part has one process plan, and all machines are 100% reliable with unlimited capacity. However, this is not realistic in manufacturing systems. Considering machines reliability in addition to machines capacity and machine duplicates during the part route selection process help to obtain better machine grouping and minimum total cost for CMS. Considering these factors in addition to operations sequence and production volumes makes the problem more complex but more realistic. Most of the methods appeared in the literature to solve such problems use mathematical programming procedures that take large amount of computational efforts. Procedures using similarity coefficient method are more flexible in incorporating various important production data and lend easily to computer applications. A new similarity coefficient equation that incorporates all these production factors is developed. Also, a procedure that captures the similarity between machine groups and minimises the total CMS cost is developed. The procedure utilises functional cells to eliminate intercellular moves and achieve ‘one-piece flow’ practise. The methodology is compared with other methods in the literature and found to be more effective.     

Molar volume of eutectic solvents as a function of molar composition and temperature☆

The conventional Rackett model for predicting liquid molar volume has been modified to cater for the effect of molar composition of the Deep Eutectic Solvents (DES). The experimental molar volume data for a group of commonly used DES has been used for optimizing the improved model. The data involved different molar compositions of each DES. The validation of the new model was performed on another set of DESs. The average relative deviation of the model on the training and validation datasets was approximately 0.1%while the Rackett model gave a relative deviation of more than 1.6%. The modified model deals with variations in DES molar com-position and temperature in a more consistent way than the original Rackett model which exhibits monotonic performance degradation as temperature moves away from reference conditions. Having the composition of the DES as a model variable enhances the practical utilization of the predicting model in diverse design and process simulation applications.     

Performance analysis of three advanced controllers for polymerization batch reactor: An experimental investigation

The performances of three advanced non-linear controllers are analyzed for the optimal set point tracking of styrene free radical polymerization (FRP) in batch reactors. The three controllers are the artificial neural network-based MPC (NN-MPC), the artificial fuzzy logic controller (FLC) as well as the generic model controller (GMC). A recently developed hybrid model (Hosen et al., 2011a. Asia-Pac. J. Chem. Eng. 6(2), 274) is utilized in the control study to design and tune the proposed controllers. The optimal minimum temperature profiles are determined using the Hamiltonian maximum principle. Different types of disturbances are introduced and applied to examine the stability of controller performance. The experimental studies revealed that the performance of the NN-MPC is superior to that of FLC and GMC.     

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.     

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.     

Dynamic modeling of gas phase propylene homopolymerization in fluidized bed reactors

A new model with comprehensive kinetics for propylene homopolymerization in fluidized bed reactors was developed to investigate the effect of mixing, operating conditions, kinetic and hydrodynamic parameters on the reactor performance as well as polymer properties. Presence of the particles in the bubbles and the excess gas in the emulsion phase was considered to improve the two-phase model, thus, considering the polymerization reaction to take place in both the bubble and emulsion phases. It was shown that in the practical range of superficial gas velocity and catalyst feed rate, the ratio of produced polymer in the bubble phase to the total production rate is roughly between 10% and 13%, which is a substantial amount and cannot be ignored. Simulation studies were carried out to compare the results of the improved two-phase, conventional well-mixed and constant bubble size models. The improved two-phase and well mixed models predicted a narrower and safer window at the same running conditions compared with the constant bubble size model. The improved two-phase model showed close dynamic behavior to the conventional models at the beginning of polymerization, but starts to diverge with the evolution of time.     

Use of silica gel for improving waterflooding performance of bottom-water reservoirs

This research addresses the problem of waterflooding a medium-gravity of oil-bearing formation with a water leg, and offers recommendations for process selection. In many reservoirs the presence of a bottom-water zone results in a very poor areal and vertical sweep efficiencies. However, waterflooding still remains the most widely used oil-recovery technique for these reservoirs. Waterflood performance in these reservoirs can be improved greatly with effective methods of partially plugging the bottom-water zone. One such method is the use of CO₂-activated silica gel as a blocking agent in the presence of a bottom-water zone.Thirteen large-model experiments were conducted using silica gel, to study the effect of oil-to-water zone permeability contrast and thickness ratio, oil viscosity, and CO₂ injection. A qualitative comparison is made to show the relative merit of CO₂-activated silica gel injection among other mobility control agents. Several runs were conducted to study silica gel rheology both in presence and in absence of CO₂.     

FA–QABC–MRTA: a solution for solving the multi-robot task allocation problem

Intelligent Service Robotics - The problem of task allocation in a multi-robot system is the situation where we have a set of tasks and a number of robots; then each task is assigned to the...     

The Role of Antibodies in the Treatment of SARS-CoV-2 Virus Infection,and Evaluating Their Contribution to Antibody-Dependent Enhancement of Infection

Antibodies play a crucial role in the immune response, in fighting off pathogens as well as helping create strong immunological memory. Antibody-dependent enhancement (ADE) occurs when non-neutralising antibodies recognise and bind to a pathogen, but are unable to prevent infection, and is widely known and is reported as occurring in infection caused by several viruses. This narrative review explores the ADE phenomenon, its occurrence in viral infections and evaluates its role in infection by SARS-CoV-2 virus, which causes coronavirus disease 2019 (COVID-19). As of yet, there is no clear evidence of ADE in SARS-CoV-2, though this area is still subject to further study.