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     

Optimal control of batch processes using particle swam optimisation with stacked neural network models

An optimal control strategy for batch processes using particle swam optimisation (PSO) and stacked neural networks is presented in this paper. Stacked neural network models are developed form historical process operation data. Stacked neural networks are used to improve model generalisation capability, as well as provide model prediction confidence bounds. In order to improve the reliability of the calculated optimal control policy, an additional term is introduced in the optimisation objective function to penalize wide model prediction confidence bounds. The optimisation problem is solved using PSO, which can cope with multiple local minima and could generally find the global minimum. Application to a simulated fed-batch process demonstrates that the proposed technique is very effective.     

On-line optimisation of culture temperature for ethanol fermentation using a genetic algorithm

The genetic algorithm (GA) was applied for on-line determination of the optimal temperature profile for ethanol fermentation. The optimisation was made by maximising the final ethanol concentration with respect to culture temperature. The effectiveness of the algorithm was examined by on-line measurements of ethanol and cell concentrations. Two computers were employed: one for monitoring, control and data processing, the other for the GA computation. A 14% increase in productivity was achieved by this method compared with the conventional constant-temperature fermentation.     

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.     

Modelling and optimisation of continuous catalytic regeneration process using bee colony algorithm

The continuous catalytic regeneration (CCR) reforming process optimisation leads to nonlinear programming with nonlinear quality constraints such as octane number and coke concentration on the catalytic particles. A typical CCR reforming process consists of four reactors with recycle. The reaction patterns and reactors have been mathematically modelled on a base of 12‐lumped kinetics reaction network derived from literature. The bee colony optimisation (BCO) algorithm is one of the most recent and efficient swarm intelligence‐based algorithms which simulates the foraging behaviour of honey bee colonies. The performance of the BCO algorithm in the process optimisation was compared with the genetic algorithm (GA). In the present work, BCO algorithm was used for optimising the CCR reforming process. The results show that the BCO algorithm reaches a better optimum point in a lower evaluation time and higher convergence rate with respect to the GA. © 2012 Canadian Society for Chemical Engineering     

Optimisation of a fermentation process for butanol production by particle swarm optimisation (PSO)

BACKGROUND: The performance of three particle swarm optimisation (PSO) algorithms was assessed in relation to their capability to optimise an alternative fermentation process for the production of biobutanol. The process consists of three interconnected units: fermentor, cell retention system and vacuum flash vessel (responsible for the continuous recovery of butanol from the broth). The dynamic behaviour of the process was described by a non‐linear mathematical model. Four constrained optimisation problems were formulated concerning the operation and design of flash fermentation: (1) maximisation of butanol productivity; (2) maximisation of substrate conversion; (3) and (4) adjustment of operating conditions in the face of problems of fluctuations in the quality of the agricultural raw material and changes in the kinetics of the microorganisms. RESULTS: The design and operation of the flash fermentation process based on the optimisation of productivity, instead of substrate conversion, resulted in a smaller fermentor and provided satisfactory values of operating conditions able to overcome problems of variations in the glucose concentration in the raw material and changes in kinetics. CONCLUSIONS: The differences among the PSO algorithms, i.e. the velocity equation and parameters values, had significant effects on the optimisation, the best results being obtained with the original velocity equation with the inertia weight decreasing linearly with each iteration. The PSO algorithms obtained solutions that obeyed constraints, demonstrating that a constraint handling method originally developed for genetic algorithms can be applied successfully to PSO algorithms. Copyright © 2010 Society of Chemical Industry     

An optimisation approach for increasing the profit of a commercial VGO hydrocracking process

In this paper, an optimisation approach is proposed to increase the profit of a commercial hydrocracking unit called Isomax. To represent the system, a full‐lump kinetic model incorporating the flow rate of fresh vacuum gas oil (VGO), bed temperatures, recycle flow rate and the catalyst life is developed. This model is capable of predicting the yield of all products, and it improves with respect to the previous works by considering LPG and light gases, fresh VGO and recycle streams as separate lumps. After developing and validating the model, the profit function of the plant, including the value of the products, fresh feed and hydrogen, as well as energy expenses, is optimised by manipulating the bed temperatures, flow rate of fresh VGO and combined feed ratio (CFR) whilst all process limitations and operating constraints are taken into account. During two years of study and considering all mechanical and operational constraints, the results confirm that the decision variables, generated by the optimisation package, can increase the gross profit of the Isomax process to about 8.17%, which is equal to $5.6 million of net profit annually. © 2012 Canadian Society for Chemical Engineering     

Strategic optimisation of biomass-based energy supply chains for sustainable mobility

The identification of alternative and sustainable energy sources has been one of the fundamental research goals of the last two decades, and the transport sector plays a key role in this challenge. Electric cars and biofuel fed vehicles may contribute to tackle this formidable issue. According to this perspective, a multi-echelon supply chain is here investigated considering biomass cultivation, transport, conversion into bioethanol or bioelectricity, distribution, and final usage in alternative bifuel (ethanol and petrol) and electric vehicles. Multiperiod and spatially explicit features are introduced in a Mixed Integer Linear Programming (MILP) modelling framework where economic (in terms of Net Present Value) and environmental (in terms of Greenhouse Gases emissions) objectives are simultaneously taken into account. The first and second generation bioethanol production supply chain is matched with a biopower production supply chain assessing multiple technologies. Both corn grain and stover are considered as biomass sources. In the environmental analysis, the impact on emissions caused by indirect Land Use Change (iLUC) effects is also assessed. Results will show the efficacy of the methodology at providing stakeholders with a quantitative tool to optimise the economic and environmental performance of different supply chain configurations.     

A hybrid optimisation model for the synthesis of sustainable gasification-based integrated biorefinery

Depletion of fossil fuels and increasing public awareness of environmental issues has stimulated the search for alternative energy sources. Biofuels are recognised as one of the most promising alternatives to fossil fuels, as they can be produced from various types of feedstock. The efficiency and sustainability of biomass-based production can be maximised by producing biofuels along with other valuable coproducts in a “biorefinery”. This concept was proposed to make the production of biofuels and biochemicals more economically viable by taking advantage of opportunities for process integration and waste recovery. In this work, a novel hybrid optimisation model that combines superstructure-based optimisation approach and insight-based automated targeting for the synthesis of a sustainable integrated biorefinery is presented. In addition, fuzzy optimisation is also adapted to synthesize such integrated facility with the simultaneous consideration of both economic and environmental performance. Note that the proposed approach is a generic synthesis strategy that can be applied even without detailed modelling of individual processes.     

Spatially explicit multi-objective optimisation for design and planning of hybrid first and second generation biorefineries

Climate change mitigation has become a binding driver in biofuels production. First generation bioethanol, initially indicated as the most competitive option, is now incurring in ever increasing discredits forcing the transition towards more sustainable productions (i.e. second and third generation technologies). This paper addresses the strategic design and planning of corn grain- and stover-based bioethanol supply chains through first and second generation technologies. A Mixed Integer Linear Programming framework is proposed to optimise the environmental and financial performances simultaneously. Multi-period, multi-echelon and spatially explicit features are embodied within the formulation to steer decisions and investments through a global approach. A demonstrative case study is proposed involving the future Italian biomass-based ethanol production. Results show the effectiveness of the optimisation tool at providing decision makers with a quantitative analysis assessing the economic and environmental performance of different design configuration and their effect in terms of technologies, plant sizes and location, and raw materials.     

The multi-period optimisation of an amine-based CO2 capture process integrated with a super-critical coal-fired power station for flexible operation

In this work, we present a model of a super-critical coal-fired power plant integrated with an amine-based CO₂ capture process. We use this model to solve a multi-period dynamic optimisation problem aimed at decoupling the operation of the power plant from the efficiency penalty imposed by the CO₂ capture plant, thus providing the power plant sufficient flexibility to exploit price variation within an electricity market. We evaluate four distinct scenarios: load following, solvent storage, exhaust gas by-pass and time-varying solvent regeneration. The objective is to maximise the decarbonised power plant's short run marginal cost profitability. It is found that while the solvent storage option provides a marginal improvement of 4% in comparison to the load following scenario, the exhaust gas bypass scenario results in a profit reduction of 17% whereas the time-varying solvent regeneration option increases the profitability of the power plant by 16% in comparison to the reference scenario.     

A multi-criteria optimisation approach for the design of sustainable utility systems

Large amounts of gaseous emissions are generated by combustion processes associated with the utility systems. The emissions include SO_x, CO₂, CO, NO_x, CH₄, and N₂O. Such emissions can result in significant impact on the surrounding environment. As a result of serious concerns about environmental problems in recent years, the design criteria for a modern utility system should include both environmental and economic requirements. This work proposes a multi-objective optimisation (MOO) strategy to identify the sustainable design of utility systems that satisfies both economic and environmental goals. A MOO mixed integer linear programming (MILP) model is developed to combine the minimisation of costs with the minimisation of environmental impact that is assessed in terms of life cycle environmental burdens. Most of the gaseous emissions are addressed in the model. The resulting MOO problem is solved using lexicographic goal programming (LGP) techniques. The new strategy has been applied to a case study for the design of a utility system with specific utility demands.     

Multi-objective optimisation using surrogate models for the design of VPSA systems

Vacuum/pressure swing adsorption is an attractive and often energy efficient separation process for some applications. However, there is often a trade-off between the different objectives: purity, recovery and power consumption. Identifying those trade-offs is possible through use of multi-objective optimisation methods but this is computationally challenging due to the size of the search space and the need for high fidelity simulations due to the inherently dynamic nature of the process. This paper presents the use of surrogate modelling to address the computational requirements of high fidelity simulations needed to evaluate alternative designs. We present SbNSGA-II ALM, surrogate based NSGA-II, a robust and fast multi-objective optimisation method based on kriging surrogate models and NSGA-II with the Active Learning MacKay (ALM) design criterion. The method is evaluated by application to an industrially relevant case study: a two column six step system for CO₂/N₂ separation. A 5 times reduction in computational effort is observed.     

Modelling and optimisation of a chemical industry wastewater treatment plant subjected to varying production schedules

Industrial wastewater treatment plants (WWTPs) have to provide 100% reliability and availability for the discharging facilities at an industrial site. Varying production schedules at these facilities and specific components occurring in the industrial wastewater considerably hinder the optimisation of industrial WWTPs. In this context it is shown in this paper that model‐based optimisation is an efficient and cost‐reducing way to ensure that an industrial WWTP functions well. The aim of the study presented was two‐fold. The first step was to show the usefulness of a proposed procedure to build and calibrate a model for the industrial WWTP. The second objective was to use the model for optimisation of the WWTP. As an example, a large set of possible production schedules in the different discharging facilities was simulated. Based on these simulations it could be predicted which schedules allow the effluent standards to be met and which do not. The calibrated and validated model was also used to investigate different operating strategies such as the in‐series operation of the two available aeration tanks. In fact, with the model it was shown that a 20% reduction of the degradable COD concentration in the effluent could be achieved by operating the tanks in series instead of in parallel. This case study shows how the approach presented can lead to fast and cost effective modelling and optimisation of an industrial WWTP. Copyright © 2004 Society of Chemical Industry     

Using a real coded PSO algorithm in the design of a multi-component countercurrent cascade

Parameter optimization of a multi-objective cascade in the multi-component mixture like maximizing of separation work relative to the number of centrifuges has too economical advantages in the centrifuge-based separation. In this paper, a cascade simulator program (CASIM) is developed to design an optimum cascade. Through CASIM, a realistic function for separation factors, α_i, in relation to θ, cut, and feed flow rate is achieved. A real coded particle swarm optimization (RCPSO) program is implemented and developed in the CASIM to design an optimum cascade. It has been shown that the application of RCPSO to this problem guarantees finding the optimum solution. It is found that all the objective functions are achieved.     

The use of physical modeling in the optimisation of a primary separation vessel feedwell

A physical modeling program was undertaken to assess and eliminate a significant vessel wear problem observed in a primary separation vessel used in the recovery of bitumen from oil sands. A 1:20 scale model was fabricated, and process rates and materials were selected on the basis of dimensional analysis. Experiments showed that the existing feedwell produced poor circumferential distribution and formed a localised jet that entered the vessel over a narrow sector. Tests were then conducted with several different modifications to the feedwell to improve the flow distribution. The final design produced uniform circumferential distribution and a modest improvement in model separation efficiency. This design has been in commercial service for several years and has eliminated the local wear problems.     

Poission search models and the evaluation of stopping rules

Mathematically convenient models for a search can be based on a prior representation of the hidden objects and their values by a mixture of Poisson processes. In simple cases, we can find the corresponding Bayes procedures to maximise expected net gains, allowing for the cost of searching. More generally, optimal stopping rules for the search axe difficult to construct and evaluate. We also investigate a procedure based on the asymptotic behaviour of the system when the number of hidden objects is large. This is shown to provide a reasonably effective stopping rule over a wide range of conditions.