Towards the modelling of a heat-exchanger reactor by a dynamic approach

The aim of this paper is to present the development of a simulation tool in order to assess the inherently safe characteristics of a heat‐exchanger reactor(HEX) operating reaction systems. The modelling of steady and transient states of a HEX reactor is performed following a hybrid dynamic approach. The global dynamic behaviour of this reactor can be represented by several continuous models, which are bounded by state or time events. Each continuous model is defined as a system of partial differential‐algebraic equations. The numerical scheme is based on the method of lines. Special attention is paid to the model initialization and a simulation strategy of the start‐up phase is presented. The validation of the model is made by numerous examples, such as the simulation of an exothermic reaction

Investigation of Model Predictive Control (MPC) for Steam Generator Level Control in Nuclear Power Plants

The capabilities and potential of Model Predictive Control (MPC) based strategies for steam generator level (SGL) controls in nuclear power plants (NPPs) have been investigated. The performance has been evaluated for all operating conditions that also include start-ups, low power operations and load rejections. These evaluations have been done for MPC controllers based on existing advanced methodologies, as well as for any potential performance improvement that can be achieved by fine tuning some of the parameters (based on the characteristics of the SGL) of the existing MPC approaches. Two version of MPC have been designed and implemented. The Standard MPC (SMPC) has investigated the performance of existing advanced MPC methodologies. The Improved MPC (IMPC) has investigated potential performance improvement over SMPC by selecting appropriate values in the weight matrix of the objective function. Performance of MPC based approaches has been evaluated and compared with an optimized PI controller in term of i) set point tracking, ii) load-following, iii) transient responses, and iv) effectiveness subject to steam and feed water flow disturbances and feed water flow signal noise. The performance evaluation has been done through computer simulation, and also through simulation on a mock-up steam generator level system. The simulation results indicate strong potential for MPC based strategies, in particular for IMPC strategy, for effective control of the steam generator levels in nuclear power plants

Cold-Start Modeling and On-Line Optimal Control of the Three-Way Catalyst

We present a three-way catalyst (TWC) cold-start model, calibrate the model based on experimental data from multiple operating points, and use the model to generate a Pareto-optimal cold-start controller suitable for implementation in standard engine control unit hardware. The TWC model is an extension of a previously presented physics-based model that predicts carbon monoxide, hydrocarbon, and nitrogen oxides tailpipe emissions. The model axially and radially resolves the temperatures in the monolith using very few state variables, thus allowing for use with control-policy based optimal control methods. In this paper, we extend the model to allow for variable axial discretization lengths, include the heat of reaction from hydrogen gas generated from the combustion engine, and reformulate the model parameters to be expressed in conventional units. We experimentally measured the temperature and emission evolution for cold-starts with ten different engine load points, which was subsequently used to tune the model parameters (e.g. chemical reaction rates, specific heats, and thermal resistances). The simulated cumulative tailpipe emission modeling error was found to be typically − 20% to + 80% of the measured emissions. We have constructed and simulated the performance of a Pareto-optimal controller using this model that balances fuel efficiency and the cumulative emissions of each individual species. A benchmark of the optimal controller with a conventional cold-start strategy shows the potential for reducing the cold-start emissions

MAB2.0 project: Integrating algae production into wastewater treatment

Different species of microalgae are highly efficient in removing nutrients from wastewater streams and are able to grow using flue gas as a CO2 source. These features indicate that application of microalgae has a promising outlook in wastewater treatment. However, practical aspects and process of integration of algae cultivation into an existing wastewater treatment line have not been investigated. The Climate-KIC co-funded Microalgae Biorefinery 2.0 project developed and demonstrated this integration process through a case study. The purpose of this paper is to introduce this process by phases and protocols, as well as report on the challenges and bottlenecks identified in the case study. These standardized technical protocols detailed in the paper help to assess different aspects of integration including biological aspects such as strain selection, as well as economic and environmental impacts. This process is necessary to guide wastewater treatment plants through the integration of algae cultivation, as unfavourable parameters of the different wastewater related feedstock streams need specific attention and management. In order to obtain compelling designs, more emphasis needs to be put on the engineering aspects of integration. Well-designed integration can lead to operational cost saving and proper feedstock treatment enabling algae growth

The systematic consideration of the large-scale fed-batch fermentation inhomogeneities using a genetically modified C. glutamicum strain as a model organism

The loss of efficiency and performance of bioprocesses on scale-up is well known, but not fully understood. This work addresses this problem, by studying the effect of some fermentation gradients (pH, glucose and oxygen) at a larger scale in a bench-scale two compartment reactor (PFR + STR) using the cadaverine-producing recombinant bacterium, Corynebacterium glutamicum DM1945 Δact3 Ptuf-ldcC_OPT. The initial scale down strategy increased the magnitude of these gradients by only increasing the mean cell residence time in the plug flow reactor (τ_PFR). The cell growth and product related rate constants were compared as the τ_PFR was increased; differences were significant in some cases, but only up to 2 min residence time. For example, losses in cadaverine productivity when compared to the control fed-batch fermentation on average for the τ_PFR of 1 min, 2 min and 5 min were 25 %, 42 % and 46 % respectively. This indicated that the increasing the τ_PFR alone does not necessarily increase the magnitude of fermentation gradients. The new scale-down strategy developed here, increased the magnitude of fermentation gradients by not only increasing the τ_PFR, but also considering the mean frequency at which the bacterial cells entered the PFR section (f_m). The f_m was kept constant by reducing the broth volume in the STR. Hence, the bacterial cells also spent shorter times in the well mixed STR, as the τ_PFR was increased (hypothesised as giving the bacterial cells less time to recover the non-ideal PFR section of the SDR). On adoption of this strategy cadaverine productivity decreases for the τ_PFR of 1 min, 2 min and 5 min were 25 %, 32 % and 53 % respectively. Thus, highlighting that loss in performance is most likely to occur as the magnitude of heterogeneity within the fermentation environment increases. However, Corynebacterium glutamicum DM1945 Δact3 Ptuf-ldcC_OPT did show some resilience in its biomass productivity. It was only marginally affected in the harshest of conditions simulated here

Algorithms and Methods for Optimizing the Spent Nuclear Fuel Allocation Strategy

Commercial nuclear power plants produce long-lasting nuclear waste, primarily in the form of spent nuclear fuel (SNF) assemblies. Spent fuel pools (SFP) and canisters or casks that sit at an independent spent fuel storage installation (ISFSI) at the reactor site store the fuel assemblies that are removed from operating reactors. The federal government has developed a plan to move the SNF from reactor sites to a Consolidated Interim Storage Facility (CISF) or a geological repository. In order to develop a predictable pick-up schedule and give utilities notice of an impending pickup from a reactor site, the federal government developed a queuing strategy based on the first-in-first-out algorithm, known as oldest fuel first (OFF). The OFF algorithm allows the federal government to remove SNF from reactor sites in the same order the assemblies came out of the reactor. While an OFF allocation strategy may result in a fair approach, it is far from the most cost-effective approach. The problem with accepting SNF using an OFF algorithm is that a handful of sites are no longer producing power and exist only to store the SNF they produced. This is an expensive process, which results in an annual cost of ~$8M [22]. Utilizing different algorithms to reduce the amount of time these shutdown reactors keep SNF on site may reduce the total system costs for the federal government. A greedy algorithm, genetic mutation algorithm, simulated annealing algorithm, and an integer programming formulation were all developed to reduce the number of years that reactors were shut down with SNF on site

Development and software implementation of modelling tools for rapid fermentation process development using a parallel mini-bioreactor system

In order to establish a generic framework for the rapid development and optimisation of scalable fermentation processes, a novel methodology for simplifying model building was explored. This approach integrates small-scale fermentations with model-based experimental design (DoE) and predictive control strategies. In this study, four 1.4 litre vessels were characterised for power input, volumetric oxygen transfer coefficient (KLa) and mixing, to assess its potential for replicating cell culture rapidly. Engineering characterisation results showed excellent propeller operation over a range of 400-1200 rpm and up to the maximum motor output and under various air flow rates in fluid densities up to 4.21 Cp/mPa s (1.211 g/cm3 ). Limits were reached using glycerol (99%) at fluid viscosities of 500Cp/mPa s (1.253g/cm3 ) at 800 rpm and no air flow, hence experiencing the most resistance. This was the most taxing condition in terms of energy input into the system. Furthermore, we determined the efficient gas dispersion which is considered important for oxygen bubble dispersion in viscous fluids. The potential gas dispersion could be calculated as a function of both impeller speed, airflow rate, and the fluid viscosity. The calculations provided a working impeller speed of >263 rpm for >0.5 vvm air flow rate as preliminary parameters in our advanced modelling section. The key outcome of the KLa study was that the results showed suitable potential for mass transfer for high cell density fermentations, for each of the parallel stirred tank bioreactors. To assess the usability of the parallel bioreactors be used for bioprocess rapid development purposes Escherichia coli W3110 was characterised in the 1L WV vessels. So overall the experiments included testing the performance of the vessels engineering parameters and also the biological fermentations confirming that the system was suitable for parallel operation with high reproducibility. For model building, especially suited for the 4-reactor set up the parallel bioreactors a fractional factorial design was used, in which models could be rapidly built and implemented for further research. The screening and model optimisation helped to reduce the development time by using the parallel equipment. Batches of four reactors could be completed in parallel in which comparable experimental results were obtained rapidly for new fermentation models. Optical density measurements provided a quick off-line analysis of the growth curve of microbial populations, as compared to cell plate counts or dry weights that require more time. For the model development and the establishment of our integrated software modelling tool, a modified logistic model was developed to predict microbial growth kinetics. First-order kinetic models, logistic, and Gompertz models were used and comparatively analysed to assess the model fit to test batch data. The logistic model was favourable for mapping and simulating the later phases of bacterial growth, while the well-established exponential growth model predicted the early lag phase in our stoichiometric growth simulation software tool better. The initialisation of the previous fermentation model allowed us to build a statistical model, which was based on the engineering characteristics for optimisation of biomass. Therefore, batch nutrient supply with the aid of stoichiometric models could be tested and modelled. DoE model data was improved with metabolic flux analysis to develop an advanced feeding strategy by testing various metabolic pathways and the nutrients used in experimentation. Bacterial growth predictions and media optimisation were tested for maximising microbial biomass yields. We then modelled the dissolved oxygen concentration and substrate utilisation. The techniques and principles of dynamic flux balance analysis, mechanistic modelling, and stoichiometric mass balancing were used. The aim was to create and validate our integrated software based on advanced modelling for the parallel bioreactor systems and tested through application for E. coli fermentations. Optimising microbial biomass was the main target in this project, with the data collected from fermentation being the strongest comparator and validator. A new software for the integration of DoE and Dynamic flux balance analysis (DFBA) techniques with the intention of creating a working fermentation platform for the Multifors equipment via simulation and fermentation optimisation was the novel outcome of this research. The tool could provide functions for speeding up development time and control of parallel bioreactors

Adaptive laboratory evolution of a genome-reduced Escherichia coli.

Synthetic biology aims to design and construct bacterial genomes harboring the minimum number of genes required for self-replicable life. However, the genome-reduced bacteria often show impaired growth under laboratory conditions that cannot be understood based on the removed genes. The unexpected phenotypes highlight our limited understanding of bacterial genomes. Here, we deploy adaptive laboratory evolution (ALE) to re-optimize growth performance of a genome-reduced strain. The basis for suboptimal growth is the imbalanced metabolism that is rewired during ALE. The metabolic rewiring is globally orchestrated by mutations in rpoD altering promoter binding of RNA polymerase. Lastly, the evolved strain has no translational buffering capacity, enabling effective translation of abundant mRNAs. Multi-omic analysis of the evolved strain reveals transcriptome- and translatome-wide remodeling that orchestrate metabolism and growth. These results reveal that failure of prediction may not be associated with understanding individual genes, but rather from insufficient understanding of the strain's systems biology