Model-based optimization of batch- and continuous crystallization processes

Crystallization is an important separation process, extensively used in most chemical industries and especially in pharmaceutical manufacturing, either as a method of production or as a method of purification or recovery of solids. Typically, crystallization can have a considerable impact on tuning the critical quality attributes (CQAs), such as crystal size and shape distribution (CSSD), purity and polymorphic form, that impact the final product quality performance indicators and inherent end-use properties, along with the downstream processability. Therefore, one of the critical targets in controlled crystallization processes, is to engineer specific properties of the final product. The purpose of this research is to develop systematic computer-aided methodologies for the design of batch and continuous mixed suspension mixed product removal (MSMPR) crystallization processes through the implementation of simulation models and optimization frameworks. By manipulating the critical process parameters (CPPs), the achievable range of CQAs and the feasible design space (FDS) can be identified. Paracetamol in water and potassium dihydrogen phosphate (KDP) in water are considered as the model chemical systems.The studied systems are modeled utilizing single and multi-dimensional population balance models (PBMs). For the batch crystallization systems, single and multi-objective optimization was carried out for the determination of optimal operating trajectories by considering mean crystal size, the distribution s standard deviation and the aspect ratio of the population of crystals, as the CQAs represented in the objective functions. For the continuous crystallization systems, the attainable region theory is employed to identify the performance of multi-stage MSMPRs for various operating conditions and configurations. Multi-objective optimization is also applied to determine a Pareto optimal attainable region with respect to multiple CQAs. By identifying the FDS of a crystallization system, the manufacturing capabilities of the process can be explored, in terms of mode of operation, CPPs, and equipment configurations, that would lead to the selection of optimum operation strategies for the manufacturing of products with desired CQAs under certain manufacturing and supply chain constraints. Nevertheless, developing reliable first principle mathematical models for crystallization processes can be very challenging due to the complexity of the underlying phenomena, inherent to population balance models (PBMs). Therefore, a novel framework for parameter estimability for guaranteed optimal model reliability is also proposed and implemented. Two estimability methods are combined and compared: the first is based on a sequential orthogonalization of the local sensitivity matrix and the second is Sobol, a variance-based global sensitivities technic. The framework provides a systematic way to assess the quality of two nominal sets of parameters: one obtained from prior knowledge and the second obtained by simultaneous identification using global optimization. A multi-dimensional population balance model that accounts for the combined effects of different crystal growth modifiers/ impurities on the crystal size and shape distribution of needle-like crystals was used to validate the methodology. A cut-off value is identified from an incremental least square optimization procedure for both estimability methods, providing the required optimal subset of model parameters. In addition, a model-based design of experiments (MBDoE) methodology approach is also reported to determine the optimal experimental conditions yielding the most informative process data. The implemented methodology showed that, although noisy aspect ratio data were used, the eight most influential and least correlated parameters could be reliably identified out of twenty-three, leading to a crystallization model with enhanced prediction capability. A systematic model-based optimization methodology for the design of crystallization processes under the presence of multiple impurities is also investigated. Supersaturation control and impurity inclusion is combined to evaluate the effect on the product's CQAs. To this end, a morphological PBM is developed for the modelling of the cooling crystallization of pure KDP in aqueous solution, as a model system, under the presence of two competitive crystal growth modifiers/ additives: aluminum sulfate and sodium hexametaphosphate. The effect of the optimal temperature control with and without the additives on the CQAs is presented via utilizing multi-objective optimization. The results indicate that the attainable size and shape attributes, can be considerably enhanced due to advanced operation flexibility. Especially it is shown that the shape of the KDP crystals can be affected even by the presence of small quantity of additives and their morphology can be modified from needle-like to spherical, which is more favourable for processing. In addition, the multi-impurity PBM model is extended by the utilization of a high-resolution finite volume (HR-FV) scheme, instead of the standard method of moments (SMOM), in order for the full reconstruction and dynamic modelling of the crystal size and shape distribution to be enabled. The implemented methodology illustrated the capabilities of utilizing high-fidelity computational models for the investigation of crystallization processes in impure media for process and product design and optimization purposes

Flexibility from local resources: Congestion management in distribution grids and carbon emission reductions

Flexibility from local energy systems has been discussed as a facilitator for the transition towards a more carbon-neutral energy system. Two use cases of this flexibility are congestion management in electricity distribution networks, and an individual-driven reduction of carbon footprints. However, for taping into this flexibility, effective incentive mechanisms and operation planning are essential. This licentiate thesis aims to provide new insights into two areas: 1) the design of market-based incentive mechanisms for congestion management in distribution grids, and 2) the operation planning of local flexible asset owners for reducing their carbon emission footprints.The first area focuses on challenges, design, and evaluation of local flexibility markets (LFMs) for congestion management in distribution grids. The utilized methods include literature review, field studies, scenario planning methods, and demonstration and simulation experiments.Results for identifying the challenges show that the most impactful and uncertain factors are the willingness and ability of end-users to participate in LFMs, and regulatory incentives for distribution system operators (DSOs). Moreover, five challenges are identified for LFM design including low market liquidity, reliability concerns, baselines, forecast errors at low aggregation levels, and the high cost of sub-meter measurements.An LFM design is proposed to address the challenges. The design is a triple horizon market structure including reservation, activation, and adjustment horizons which can support decision making of market participants and improve market liquidity and reliability. Adapted capacity-limitation products are proposed that are calculated based on net-load and subscribed connection capacity of end-users. The products can reduce conflict of interests, and administrative and sub-meter measurement costs related to delivery validation and baselines. Moreover, probabilistic approaches for calculating the cost and value of the products are proposed that can reduce the potential cost of forecast errors for market participants while providing insights on how the utility and cost of the products can be calculated.Evaluating the proposed design is an ongoing work utilizing simulations and real-life demonstrations. The most suitable congestion management solution can vary depending on the context and test-system. Therefore, the evaluation should include comparing the design with other congestion management solutions such as power tariffs. A comparison toolbox is proposed to be used by researchers and DSOs including a qualitative comparison framework and a reusable modeling platform for the quantitative comparison. Four cases are quantitatively compared using the toolbox on a sub-area of Chalmers campus testbed: i) LFM+PT+ET (i.e., considering the LFM, power tariff (PT), and energy cost (ET) simultaneously), ii) LFM+ET, iii) PT+ET, and iv) ET. The most recent results show that case (i), has the lowest number of congested hours. Moreover, congestions due to rebound effects from activating the LFM are observed. The comparison of cases (i) and (ii) suggests that enforcing power tariffs besides the LFM can reduce the rebound effects.The second area utilizes a multi-objective optimization model for identifying CO2 emission abatement strategies and their cost for Chalmers testbed local multi-energy system. The results of the case study show that the carbon emission footprint of the local system can be reduced by 20.8% with a 2.2% increase in the cost. The operation strategies for this purpose include more usage of biomass boilers in heat production, substitution of district heating and absorption chillers with heat pumps, and higher utilization of storage. The cost of the strategies ranged from 36.6-100.2 €/tCO2.This thesis can benefit system operators, flexibility asset owners, policy makers, and researchers dealing with local flexibility resources by offering insights into the challenges and proposing solutions and toolboxes for implementation and evaluation

Sustainable Construction Engineering and Management

This Book is a Printed Edition of the Special Issue which covers sustainability as an emerging requirement in the fields of construction management, project management and engineering. We invited authors to submit their theoretical or experimental research articles that address the challenges and opportunities for sustainable construction in all its facets, including technical topics and specific operational or procedural solutions, as well as strategic approaches aimed at the project, company or industry level. Central to developments are smart technologies and sophisticated decision-making mechanisms that augment sustainable outcomes. The Special Issue was received with great interest by the research community and attracted a high number of submissions. The selection process sought to balance the inclusion of a broad representative spread of topics against research quality, with editors and reviewers settling on thirty-three articles for publication. The Editors invite all participating researchers and those interested in sustainable construction engineering and management to read the summary of the Special Issue and of course to access the full-text articles provided in the Book for deeper analyses

PV Charging and Storage for Electric Vehicles

Electric vehicles are only ‘green’ as long as the source of electricity is ‘green’ as well. At the same time, renewable power production suffers from diurnal and seasonal variations, creating the need for energy storage technology. Moreover, overloading and voltage problems are expected in the distributed network due to the high penetration of distributed generation and increased power demand from the charging of electric vehicles. The energy and mobility transition hence calls for novel technological innovations in the field of sustainable electric mobility powered from renewable energy. This Special Issue focuses on recent advances in technology for PV charging and storage for electric vehicles