Multi-objective optimization of nonlinear switched time-delay systems in fed-batch process

© 2016 Elsevier Inc.Maximization of productivity and minimization of consumption are two top priorities for biotechnological industry. In this paper, we model a fed-batch process as a nonlinear switched time-delay system. Taking the productivity of target product and the consumption rate of substrate as the objective functions, we present a multi-objective optimization problem involving the nonlinear switched time-delay system and subject to continuous state inequality constraints. To solve the multi-objective optimization problem, we first convert the problem into a sequence of single-objective optimization problems by using convex weighted sum and normal boundary intersection methods. A gradient-based single-objective solver incorporating constraint transcription technique is then developed to solve these single-objective optimization problems. Finally, a numerical example is provided to verify the effectiveness of the numerical solution approach. Numerical results show that the normal boundary intersection method in conjunction with the developed single-objective solver is more favourable than the convex weighted sum method

Macroeconomics modelling on UK GDP growth by neural computing

This paper presents multilayer neural networks used in UK gross domestic product estimation. These networks are trained by backpropagation and genetic algorithm based methods. Different from backpropagation guided by gradients of the performance, the genetic algorithm directly evaluates the performance of multiple sets of neural networks in parallel and then uses the analysed results to breed new networks that tend to be better suited to the problems in hand. It is shown that this guided evolution leads to globally optimal networks and more accurate results, with less adjustment of the algorithm needed

Optimal parameter selection for nonlinear multistage systems with time-delays

In this paper, we consider a novel dynamic optimization problem for nonlinear multistage systems with time-delays. Such systems evolve over multiple stages, with the dynamics in each stage depending on both the current state of the system and the state at delayed times. The optimization problem involves choosing the values of the time-delays, as well as the values of additional parameters that influence the system dynamics, to minimize a given cost functional. We first show that the partial derivatives of the system state with respect to the time-delays and system parameters can be computed by solving a set of auxiliary dynamic systems in conjunction with the governing multistage system. On this basis, a gradient-based optimization algorithm is proposed to determine the optimal values of the delays and system parameters. Finally, two example problems, one of which involves parameter identification for a realistic fed-batch fermentation process, are solved to demonstrate the algorithm’s effectiveness

Optimizing the switching operation in monoclonal antibody production: Economic MPC and reinforcement learning

Monoclonal antibodies (mAbs) have emerged as indispensable assets in medicine, and are currently at the forefront of biopharmaceutical product development. However, the growing market demand and the substantial doses required for mAb clinical treatments necessitate significant progress in its large-scale production. Most of the processes for industrial mAb production rely on batch operations, which result in significant downtime. The shift towards a fully continuous and integrated manufacturing process holds the potential to boost product yield and quality, while eliminating the extra expenses associated with storing intermediate products. The integrated continuous mAb production process can be divided into the upstream and downstream processes. One crucial aspect that ensures the continuity of the integrated process is the switching of the capture columns, which are typically chromatography columns operated in a fed-batch manner downstream. Due to the discrete nature of the switching operation, advanced process control algorithms such as economic MPC (EMPC) are computationally difficult to implement. This is because an integer nonlinear program (INLP) needs to be solved online at each sampling time. This paper introduces two computationally-efficient approaches for EMPC implementation, namely, a sigmoid function approximation approach and a rectified linear unit (ReLU) approximation approach. It also explores the application of deep reinforcement learning (DRL). These three methods are compared to the traditional switching approach which is based on a 1% product breakthrough rule and which involves no optimization

Modelling concentration gradients in fed‐batch cultivations of E. coli – towards the flexible design of scale‐down experiments

BACKGROUND: The impact of concentration gradients in large industrial-scale bioreactors on microbial physiology can be studied in scale-down bioreactors. However, scale-down systems pose several challenges in construction, operation and footprint. Therefore, it is challenging to implement them in emerging technologies for bioprocess development, such as in high throughput cultivation platforms. In this study, a mechanistic model of a two-compartment scale-down bioreactor is developed. Simulations from this model are then used as bases for a pulse-based scale-down bioreactor suitable for application in parallel cultivation systems. RESULTS: As an application, the pulse-based system model was used to study the misincorporation of non-canonical branched-chain amino acids into recombinant pre-proinsulin expressed in Escherichia coli, as a response to oscillations in glucose and dissolved oxygen concentrations. The results show significant accumulation of overflow metabolites, up to 18.3 % loss in product yield and up to 10 fold accumulation of the non-canonical amino acids norvaline and norleucine in the product in the pulse-based cultivation, compared to a reference cultivation. CONCLUSIONS: Our results indicate that the combination of a pulse-based scale-down approach with mechanistic models is a very suitable method to test strain robustness and physiological constraints at the early stages of bioprocess development.EC/H2020/643056/EU/Rapid Bioprocess Development/Biorapi

Time-delay estimation for nonlinear systems with piecewise-constant input

We consider a general nonlinear time-delay system in which the input signal is piecewise-constant. Such systems arise in a wide range of industrial applications, including evaporation and purification processes and chromatography. We assume that the time-delays—one involving the state variables and the other involving the input variables—are unknown and need to be estimated using experimental data. We formulate the problem of estimating the unknown delays as a nonlinear optimization problem in which the cost function measures the least-squares error between predicted and measured system output. The main difficulty with this problem is that the delays are decision variables to be optimized, rather than fixed values. Thus, conventional optimization techniques are not directly applicable. We propose a new computational approach based on a novel algorithm for computing the cost function’s gradient. We then apply this approach to estimate the time-delays in two industrial chemical processes: a zinc sulphate purification process and a sodium aluminate evaporation process

Perturbation-based Control of Industrial Fed-batch Bioprocesses

The topic of this thesis is bioprocess control, more specifically control of industrial-scale microbial fed-batch bioprocesses. Its focus is therefore on methods which are easy to implement in an industrial setting, which gives certain limitations on sensors, actuators and control systems. The main part of the work in the thesis concerns control of the microbial substrate uptake rate by manipulation of the feed rate of liquid substrate to the bioprocess. This is an important parameter for improving process yields, as too low feed rates cause starvation of the microorganisms while too high rates lead to production of undesirable by-products. By-product formation decreases metabolic efficiency and the by-products have inhibiting effects on microbial growth and production. At high concentrations these can even halt growth completely, leading to process failure. Due to large batch-to-batch variations and the complexity of the pro- cesses, model-based control can be difficult to use in this type of system. The approach used in this thesis circumvents this problem by utilizing perturbations in the feed rate. It has previously been shown that the metabolic state with regard to substrate uptake rate can be determined by analysing the perturbation response in the dissolved oxygen level of a microbial process. In this thesis, the concept is developed through the use of perturbations at a predefined frequency. This provides a number of advantages and allows for estimation of the metabolic state through observing the perturbation frequency in the measured signal. The concept has been tested experimentally in industrial pilot and pro- duction scale. It has been demonstrated that a controller based on this concept can be used to compensate for batch-to-batch variations in feed demand and can rapidly compensate for changes in the demand. It has also been shown that the method can be used for monitoring and control in bioprocesses with a volume over 100 m3, using a low-complexity estimation algorithm suited for industrial use. The thesis also concerns mid-ranging control in non-stationary processes. A modified mid-ranging controller suited for such processes is proposed, which allows control signals to increase in unison during the course of a fed-batch process while maintaining the advantages of classical mid-ranging control. The concept can for instance be used for control of dissolved oxygen, an important process parameter in many bioprocesses. It has been successfully used for this purpose in pilot scale alongside the type of perturbationbased feed rate controller which is the main topic of this thesis, also showing how the latter can be used in conjunction with other control systems

Fast and flexible mRNA vaccine manufacturing as a solution to pandemic situations by adopting chemical engineering good practice: continuous autonomous operation in stainless steel equipment concepts

SARS-COVID-19 vaccine supply for the total worldwide population has a bottleneck in manufacturing capacity. Assessment of existing messenger ribonucleic acid (mRNA) vaccine processing shows a need for digital twins enabled by process analytical technology approaches in order to improve process transfer for manufacturing capacity multiplication, a reduction in out-of-specification batch failures, qualified personal training for faster validation and efficient operation, optimal utilization of scarce buffers and chemicals and speed-up of product release by continuous manufacturing. In this work, three manufacturing concepts for mRNA-based vaccines are evaluated: Batch, full-continuous and semi-continuous. Technical transfer from batch single-use to semi-continuous stainless-steel, i.e., plasmid deoxyribonucleic acid (pDNA) in batch and mRNA in continuous operation mode, is recommended, in order to gain: faster plant commissioning and start-up times of about 8–12 months and a rise in dose number by a factor of about 30 per year, with almost identical efforts in capital expenditures (CAPEX) and personnel resources, which are the dominant bottlenecks at the moment, at about 25% lower operating expenses (OPEX). Consumables are also reduceable by a factor of 6 as outcome of this study. Further optimization potential is seen at consequent digital twin and PAT (Process Analytical Technology) concept integration as key-enabling technologies towards autonomous operation including real-time release-testing

Hydrocracking of short residue over unsupported and supported magnetite nanocatalysts.

Masters Degree. University of KwaZulu-Natal, Durban.With the rapid depletion of crude oil and current cracking methods of heavy petroleum residue all resulting in the production of undesirable coke formation, a better solution must be found. This project investigated the use of an unsupported molybdenum-doped magnetite nano-catalyst, as well as a magnetite nanocatalyst on a mesoporous silica support, to determine if the use of these catalysts can be successful in cracking petroleum residue. Short residue from the vacuum distillation column supplied by SAPREF, was used throughout the experimental work. A lot of effort went into the preparation of the feedstock due to the high viscosity of short residue. The solvent used during experimental work was toluene, which was used to dilute the short residue. A temperature range between 350˚C and 400˚C was used in order to determine temperature effects on product distribution from the cracking reaction. The feedstock to catalyst ratio was also varied, using the unsupported catalyst, in order to determine the effects of the amount of catalyst on the reaction. Kerosene and gas oil are the desired products due to their higher heating value and use as liquid fuels compared to the heavier residue. There is a strong interaction between temperature and catalyst to feedstock ratio. The high temperature-high catalyst combination gave improved gas oil yields over the low temperature-high catalyst combination. Results carried out at 400˚C with a high catalyst amount showed the most favourable results with a yield of 49.3% and 6% of gas oil and kerosene respectively. Aquaprocessing (catalytic splitting of water that occurs on the surface complexes of the iron-based catalyst, at a relatively low pressure) was simulated at the experimental conditions using kinetics from literature for a nickel-based catalyst. The simulated composition profiles proved that the unsupported magnetite nanocatalyst was much more efficient in upgrading residue than the nickel based catalyst, due to the presence of greater amounts of lighter components. Analysis of the catalyst after the cracking reaction shows that no major phase changes had taken place and that the catalyst could be regenerated to be used again. The supported magnetite nanocatlyst was compared to conventional nickel-molybdenum and cobalt-molybdenum catalyst, in a fixed bed reactor set up. The supported catalyst proved to be the most consistent, and was able to shift the residue into the lighter fractions more effectively than the conventional catalysts. The supported catalyst was the most effective in cracking the vacuum residue, mostly into vacuum gas oil. The yields using the catalyst compared quite favourably with the unsupported catalyst, with the unsupported catalyst yielding more lighter components. The most favourable results implementing a supported catalyst were also at 400˚C, due to the extensive decrease in vacuum residue and a corresponding increase in lighter components. Ultimately this investigation proved that hydrocracking can take place with the use of a supported and unsupported magnetite nanocatalyst, at lower temperatures than that of conventional methods and aquaprocessing. It was also proven that the process can be upscaled to industry level, as shown with the performance of the supported catalyst. A larger temperature range could give better clarity in the performance of the catalyst for future petroleum residue cracking.Examiner's copy