Reliability of complex chemical engineering processes

This paper presents a stochastic performance modelling approach that can be used to optimise design and operational reliability of complex chemical engineering processes. The framework can be applied to processes comprising multiple units, including the cases where closed form process performance functions are unavailable or difficult to derive from first principles, which is often the case in practice. An interface that facilitates automated two-way communication between Matlab^® and process simulation environment is used to generate large process responses. The resulting constrained optimisation problem is solved using both Monte Carlo Simulation (MCS) and First Order Reliability Method (FORM); providing a wide range of stochastic process performance measures. Adding such capabilities to traditional deterministic process simulators provides a more informed basis for selecting optimum design factors; giving a simple way of enhancing overall process reliability and cost-efficiency. Two case study systems are considered to highlight the applicability and benefits of the approach.     

SIMULATION AND SCALE-UP OF PNEUMATIC CONVEYING AND CASCADING ROTARY DRYERS

ABSTRACT

This paper presents a unified model for simulation of cocurrent and countercurrent dispersion-type dryers. The main industrial applications are to pneumatic conveying (flash) dryers and cascading (direct) rotary dryers. The basic model is a one-dimensional incremental (stepwise) simulation, which has been developed over a number of years Equations for particle motion, heat and mass transfer, heat and mass balances and local gas conditions are solved simultaneously over a small increment along the dryer. All workers have previously had considerable difficulty in obtaining a good fit between simulations and actual results from pilot-plants or large-scale industrial dryers. A new “fitting mode” calculation overcomes this by identifying the parameters which need to be adjusted, concentrating on those which cannot be measured accurately. Excellent agreement has been obtained between the model and experimental data by this method. The paper also presents revised formulations for particle motion and heat transfer in rotary dryers. The model has been incorporated into two computer programs for flash and rotary dryers respectively, and results from the former are shown for a case study.     

A SEMI-EMPIRICAL APPROACH TO OPTIMISE THE QUANTITY OF DRYING AIDS REQUIRED TO SPRAY DRY SUGAR-RICH FOODS

ABSTRACT

A semi-empirical linear equation has been developed to optimise the amount of maltodextrin additive (DE 6) required to successfully spray dry a sugar-rich product on the basis of its composition. Based on spray drying experiments, drying index values for individual sugars (sucrose, glucose, fructose) and citric acid were determined, and using these index values an equation for model mixtures of these components was established. This equation has been tested with two sugar-rich natural products, pineapple juice and honey. The relationship was found to be valid for these products.     

Fluid catalytic cracking optimisation using factorial design and genetic algorithm techniques

Statistical techniques coupled with genetic algorithm (GA) were used to identify optimal values of key operational variables in fluid catalytic cracking (FCC) process. A Kellog Orthoflow F fluid catalytic cracking process model was considered. It is known as a highly nonlinear process with a large number of variables with strong interactions among them. A reduced process model was obtained through factorial design technique to be used as a process function in the optimisation work giving as result the operational conditions that maximise conversion without infringing operational restrictions with savings in computational burden and time. An increase of 8.71% in process conversion was achieved applying GA as optimisation technique. © 2012 Canadian Society for Chemical Engineering     

Application of genetic algorithms in design and optimisation of multi‐stream plate–fin heat exchangers

This paper introduces two new approaches in the thermo‐hydraulic design of multi‐stream heat exchangers (MSHEs). In both approaches, geometrical aspects of the MSHE (e.g. exchanger dimensions, fin type, etc.) are optimised with a genetic algorithm (GA) using the Total Annual Cost (TAC) as an objective function. The first approach is capable of utilising the maximum allowable stream pressure drops and can result in minimal surface area requirements. In the second approach, all of the pressure drop values are considered as design variables and are therefore subject to optimisation. These approaches have been applied to two case studies taken from literature, and the results are compared to those arising from a currently used design method. In the first case study, application of the new approaches resulted in a smaller TAC than the current approach by 5.77% and 31.86%, respectively, and improvement in the second case was estimated to be 5% and 21.46%, correspondingly. The effect of different fins on an MSHE's TAC is discussed through application of GAs to the current approach. It is shown that correct selection of fin types reduces the TAC of the two case studies by 21.75% and 8.7%, respectively. © 2012 Canadian Society for Chemical Engineering     

Semicontinuous separation of dimethyl ether (DME) produced from biomass

A ternary semicontinuous system for the separation of bio‐dimethyl ether from methanol and water is presented. The performance of eight potential control configurations, including the application of temperature inferential control, is evaluated. Dynamic simulations of the semicontinuous system and associated control scheme demonstrate that the temperature inferential control configuration is effective in achieving the separation objectives while remaining within operational limits. The semicontinuous system using the inferential temperature control scheme is simulated and shown to be economically preferable to the traditional continuous process for a range of production rates. © 2013 Canadian Society for Chemical Engineering     

Process analysis and optimisation of hybrid processes for the dehydration of ethanol

Promising hybrid processes for ethanol dewatering consist of different combinations of distillation with adsorption and/or vapour permeation. This paper presents an analysis and optimisation of these hybrid processes using non-equilibrium models and an evolutionary algorithm. Four different membrane assisted configurations are compared with a benchmark process consisting of distillation and pressure swing adsorption. In total 12 cases were investigated while assuming different feed and product compositions at different production capacities: three ethanol mass fractions in feed 45, 80, 92 wt.%, two product purities 99.6, 99.95 wt.% and two production capacities 25,000, 250,000 m³/year. The influence of decisive operating and structural variables on important target variables such as total membrane area is demonstrated. Finally, the processes are evaluated regarding operating costs and energy consumption depending on product purity and production capacity. The operating costs of the membrane assisted configurations differ only in a small range of −3% to 6% from those of the benchmark. The energy consumption of the membrane assisted configurations without distillation is up to 30% lower compared to the benchmark. Especially the combination of vapour permeation and adsorption is a promising alternative allowing for producing ethanol with high purities at lower operating pressures compared to the vapour permeation as stand alone process.     

A multi-objective particle swarm optimisation for filter-based feature selection in classification problems

Feature selection has the two main objectives of minimising the classification error rate and the number of features. Based on binary particle swarm optimisation (BPSO), we develop two novel multi-objective feature selection frameworks for classification, which are multi-objective binary PSO using the idea of non-dominated sorting (NSBPSO) and multi-objective binary PSO using the ideas of crowding, mutation and dominance (CMDBPSO). Four multi-objective feature selection methods are then developed by applying mutual information and entropy as two different filter evaluation criteria in each of the proposed frameworks. The proposed algorithms are examined and compared with a single objective method on eight benchmark data sets. Experimental results show that the proposed multi-objective algorithms can evolve a set of solutions that use a smaller number of features and achieve better classification performance than using all features. In most cases, NSBPSO achieves better results than the single objective algorithm and CMDBPSO outperforms all other methods mentioned above. This work represents the first study on multi-objective BPSO for filter-based feature selection.     

Optimal operating strategies for emulsion terpolymerisation

Optimal control policies for emulsion terpolymerisation of styrene, methyl methacrylate (MMA) and methyl acrylate (MA) were determined in a semi-batch reactor using the multi-objective dynamic optimisation method. A comprehensive dynamic model was used for the design of optimal control. The control vector parameterisation (CVP) approach was implemented for constrained optimisation for emulsion terpolymerisation reactors. The feed rates of styrene, MMA, MA, surfactant and initiator, and the temperature of the reactor were used as manipulating variables to produce terpolymers of desired composition, molecular weight distribution (MWD) and particle size distribution (PSD). The particle size polydispersity index (PSPI), molecular weight polydispersity index (MWPI) and the overall terpolymer composition ratios were incorporated in the objective functions to optimise the PSD, MWD and terpolymer composition, respectively. The optimised operational policies were validated with experiments via one stirred tank polymerisation reactor.