Active heat exchange system development for latent heat thermal energy storage

Active heat exchange concepts for use with thermal energy storage systems in the temperature range of 250 C to 350 C, using the heat of fusion of molten salts for storing thermal energy are described. Salt mixtures that freeze and melt in appropriate ranges are identified and are evaluated for physico-chemical, economic, corrosive and safety characteristics. Eight active heat exchange concepts for heat transfer during solidification are conceived and conceptually designed for use with selected storage media. The concepts are analyzed for their scalability, maintenance, safety, technological development and costs. A model for estimating and scaling storage system costs is developed and is used for economic evaluation of salt mixtures and heat exchange concepts for a large scale application. The importance of comparing salts and heat exchange concepts on a total system cost basis, rather than the component cost basis alone, is pointed out. The heat exchange concepts were sized and compared for 6.5 MPa/281 C steam conditions and a 1000 MW(t) heat rate for six hours. A cost sensitivity analysis for other design conditions is also carried out

Resource effiency indicator-based decision support for the operation of batch and mixed batch-continuous processing plants

Steigende Konzentrationen von Treibhausgasen in der Atmosphäre sind der Grund für den globalen Klimawandel. Da die chemische Industrie wesentlich zu den Treibhausgasemissionen beiträgt, schaffen politische Entscheidungsträger Anreize und Gesetze, um die Industrie zu einer nachhaltigeren Produktion zu bewegen. In dieser Arbeit wird ein Rahmen zur Definition und Nutzung von Echtzeit-Ressourcene zienzindikatoren (REI) entwickelt, um die Ressourceneffizienz industrieller Produktionsprozesse kontinuierlich zu überwachen und zu optimieren. Die Ressourceneffizienz ist eine mehrdimensionale Größe, die in Relation zur Wirtschaftlichkeit bewertet werden kann. Der Fokus der Arbeit liegt dabei auf Batch-Prozessen und Prozessen, die diskontinuierliche und kontinuierliche Teilprozesse kombinieren. Diese stellen eine Herausforderung für die korrekte Erfassung relevanter Prozessgrößen und die anschlie ende Analyse dar. Das vorgeschlagene Propagationskonzept ermöglicht es, den Gesamtwirkungsgrad der Anlage auf Basis der Leistung ihrer Komponenten zu berechnen. Die daraus resultierenden REIs spiegeln die technische Leistung der Anlage wieder und werden zur Optimierung der gesamten Ressourceneffizienz eines Anwendungsbeispiels verwendet. Die Optimierung der Ressourceneeffizienz stellt ein mehrdimensionales Optimierungsproblem dar, bei dem die Pareto-optimalen Betriebspunkte die möglichen Kompromisse zwischen konkurrierenden Interessen angeben. Die Auswahl eines gewünschten Betriebspunktes aus der Paretomenge ist nicht trivial und kann sich ändernden Präferenzen folgen. Daher befasst sich der zweite Teil der Arbeit mit der Synthese eines effizienten und effektiven Entscheidungsunterstützungssystems (Decision Support System, DSS) zur Auswahl eines Betriebspunktes mit dem gewünschten Leistungsprofil. Die Methodik wird auf ein Beispiel angewendet und durch eine experimentelle Usability-Studie validiert. Damit leistet diese Arbeit einen Beitrag zur Optimierung der Ressourceneffizienz in der Prozessindustrie durch die Identifikation von ressourcenoptimalen Betriebszuständen. Die ganzheitliche Betrachtung der Ressourceneffizienz in Batchprozessen stellt eine wichtige Erweiterung der industriellen Praxis dar, die sich derzeit in der Regel auf eine Energieeffizienzanalyse nach ISO50001 beschränkt.Increasing concentrations of greenhouse gases (GHG) in the atmosphere are the reason for global climate change. Since the chemical industry is a signficant contributor to the GHG emissions, policy makers are creating incentives and legislation to steer the industry towards a more sustainable production. This thesis proposes a framework to defie and utilize real-time resource effiency indicators (REI) to constantly monitor and optimize the resource effiency of industrial production processes. Resource effiency is a multidimensional entity that can be evaluated in relation to the economic performance. The focus of the thesis is on batch- and hybrid - coupled batch and continuously operated -{ processes that introduce further challenges for the correct recording of relevant process variables and the subsequent analysis. The proposed propagation concept makes it possible to calculate the overall effiency of the plant based on the performance of its components. The resulting REIs reflect the technical performance of the plant and are used to optimize the overall resource effiency of an application case. Optimizing the resource effiency of a process poses a multi-dimensional optimization problem, where the Pareto optimal operating points reflect the potential trade-offs between competing interests. The selection of a desired operational point among the optimal set is not trivial and may be subject to changing preferences. Thus, the second part of the thesis addresses the synthesis of an effcient and effective decision support system (DSS) to select an operating point with the desired performance profile. The methodology is applied and validated by an experimental usability-study. In summary, the thesis contributes to the optimization of resource effiency in the process industry by identifying resource-optimal operating conditions. The holistic consideration of resource effiency in batch processes represents an important extension of industrial practice, which is up to now usually limited to an energy effiency analysis according to ISO50001

Modeling, optimization, and sensitivity analysis of a continuous multi-segment crystallizer for production of active pharmaceutical ingredients

We have investigated the simulation-based, steady-state optimization of a new type of crystallizer for the production of pharmaceuticals. The multi-segment, multi-addition plug-flow crystallizer (MSMA-PFC) offers better control over supersaturation in one dimension compared to a batch or stirred-tank crystallizer. Through use of a population balance framework, we have written the governing model equations of population balance and mass balance on the crystallizer segments. The solution of these equations was accomplished through either the method of moments or the finite volume method. The goal was to optimize the performance of the crystallizer with respect to certain quantities, such as maximizing the mean crystal size, minimizing the coefficient of variation, or minimizing the sum of the squared errors when attempting to hit a target distribution. Such optimizations are all highly nonconvex, necessitating the use of the genetic algorithm. Our results for the optimization of a process for crystallizing flufenamic acid showed improvement in crystal size over prior literature results. Through the use of a novel simultaneous design and control (SDC) methodology, we have further optimized the flowrates and crystallizer geometry in tandem.^ We have further investigated the robustness of this process and observe significant sensitivity to error in antisolvent flowrate, as well as the kinetic parameters of crystallization. We have lastly performed a parametric study on the use of the MSMA-PFC for in-situ dissolution of fine crystals back into solution. Fine crystals are a known processing difficulty in drug manufacture, thus motivating the development of a process that can eliminate them efficiently. Prior results for cooling crystallization indicated this to be possible. However, our results show little to no dissolution is used after optimizing the crystallizer, indicating the negative impact of adding pure solvent to the process (reduced concentration via dilution, and decreased residence time) outweighs the positive benefits of dissolving fines. The prior results for cooling crystallization did not possess this coupling between flowrate, residence time, and concentration, thus making fines dissolution significantly more beneficial for that process. We conclude that the success observed in hitting the target distribution has more to do with using multiple segments and having finer control over supersaturation than with the ability to go below solubility. Our results showed that excessive nucleation still overwhelms the MSMA-PFC for in-situ fines dissolution when nucleation is too high

Synthesis, optimisation and control of crystallization systems

Process systems engineering has provided with a range of powerful tools to chemical engineers for synthesis, optimisation and control using thorough understanding of the processes enhanced with the aid of sophisticated and accurate multi-faceted mathematical models. Crystallization processes have rarely benefited from these new techniques, for they lack in models that could be used to bridge the gaps in their perception before utilising the resulting insight for the three above mentioned tasks. In the present work, first a consistent and sufficiently complex models for unit operations including MSMPR crystallizer, hydrocyclone and fines dissolver are developed to enhance the understanding of systems comprising these units. This insight is then utilised for devising innovative techniques to synthesise, optimise and control such processes. A constructive targeting approach is developed for innovative synthesis of stage-wise crystallization processes. The resulting solution surpasses the performance obtained from conventional design procedure not only because optimal temperature profiles are used along the crystallizers but also the distribution of feed and product removal is optimally determined through non-linear programming. The revised Machine Learning methodology presented here for continual process improvement by analysing process data and representing the findings as zone of best average performance, has directly utilised the models to generate the data in the absence of real plant data. The methodology which is demonstrated through KNO₃ crystallization process flowsheet quickly identifies three opportunities each representing an increase of 12% on nominal operation. An optimal multi-variable controller has been designed for a one litre continuous recycle crystallizer to indirectly control total number and average size of crystals from secondary process measurements. The system identification is solely based on experimental findings. Linear Quadratic Gaussian method based design procedure is developed to design the controller which not only shows excellent set-point tracking capabilities but also effectively rejects disturbance in the simulated closed loop runs

Modelling crystal growth from pure and impure solutions : a case study on sucrose

Tese de doutoramento. Engenharia Química. 2006. Faculdade de Engenharia. Universidade do Port

Advances in Industrial Crystallization

This Special Issue is result of a call for papers of the Section Industrial Crystallization of MDPI’s scientific journal Crystals. It addresses scientists and engineers active in research and process & product development in life-science industries (e.g. pharmaceuticals, fine chemicals and biotechnology products) and bulk chemical applications (e.g. desalination) as well. The contributions comprise several fundamental and application-oriented facets of crystallization providing an overview of industrially relevant subjects in the field. Main issues cover phase equilibria and solid-state behavior of crystalline compounds, crystal shape and size and related measurement techniques. Melt and solution crystallization are considered specifically addressing contemporary aspects of continuous crystallization and process intensification

Sustainable nutrient recovery from animal manure: A review of current best practice technology and the potential for freeze concentration

Current trends of livestock expansion and associated mass production of manure bring a net import of nutrients that have led to a significant excess in many areas. The implementation of an efficient and more economical technology solution to recover and re-use nutrients from raw or digested wastes is essential and will reduce the need for fossil-fuel based fertilizers. From a waste management standpoint, the identification of nutrient recovery technologies is considered one of the main challenges within a circular economy context. Several traditional techniques exist for manure treatment such as, gasification, thermochemical conversion, composting, hydrothermal carbonization, and liquefaction. However, these technologies face many challenges related to energy consumption and recovered nutrient quality. In this context, freeze concentration (FC) is an emerging technique that can be applied to recover water and concentrate nutrients from waste liquid effluents. This technology brings advantages such as high concentration factor and low energy usage. However, freeze concentration technology is only semi-industrialised and for most applications remains at the development stage. Many studies have been conducted to design and develop processes and applications that target the improvement of both productivity and efficiency, which makes freeze concentration an attractive research subject to the scientific community. Combination of freeze concentration technology with another technology, such as membranes, to generate a more efficient hybrid process must also be considered. This approach of resource recovery from animal manure would ultimately create a more sustainable and circular economy. This paper evaluates the current state-of-the-art and processing strategies related to the treatment of livestock waste materials and contains an up-to-date and critical review on nutrient-rich effluent valorization technologies; focusing on the latest technological progress to recover nutrients from animal manure and introduces the potential that freeze concentration offers, which has only been marginally explored to date. This work makes a comparative analysis of the different processes in terms of their efficiency, cost, energy consumption, operational management, and the results obtained from both bench and large-scale experiments; making it possible to determine the current best practice procedures for the treatment of animal manure

A BRIEF REVIEW ON PROCESS ANALYTICAL TECHNOLOGY (PAT)

Process analytical technology (PAT) has been defined as a mechanism to design, analyze and control pharmaceutical manufacturing processes through measurement of critical process parameters which affect critical quality attributes. PAT checks the quality of raw material attributes both physically and chemically (i.e. at off-line, on-line, in-line). PAT involves a shift from testing the quality of building to the quality of products by testing at several intermediate steps. PAT saves a huge amount of time and money required for sampling and analysis of products. The main goal of PAT is to provide successful tools such as multivariate data analysis and acquisition tools, modern process analyzers or analytical chemistry, endpoint process monitoring, controlling tools and continuous improvement and knowledge improvement tools. In this review attempt has been carried out to explore the concept of PAT, different tools of PAT, goals of PAT, How it Works and Its benefits

Predictive modelling of organic crystallization processes.

This thesis is concerned with the development of a predictive model for batch cooling suspension pharmaceutical crystallizations, with a focus on product performance. A major challenge involved in the design of industrial pilot plant pharmaceutical crystallizers, is to predict the influence of crystallizer geometry, scale and operating conditions on the process behaviour and crystal size distribution (CSD). The design of industrial crystallizers is hindered by the lack of scale-up rules due to the absence of reliable predictive process models. Currently no reliable predictive or 'dial up a particle size' tool exists for scale-up predictions. The research involves the development of a novel predictive compartmental modelling framework for the scale-up of an organic fine chemical. A new approach of using compartments is developed in order to facilitate scale-up design and process modelling by separating crystallization kinetic and hydrodynamic phenomena. Application of this technique involves determining key process engineering information on a laboratory scale, which is critical for technology transfer, and combining this data with hydrodynamic information on transfer to large scale for predictive scale-up purposes. The key process engineering information required for predictive modelling includes the determination of solubility characteristics, thermodynamic properties and crystallization kinetics of the organic fine chemical. Attenuated Total Reflectance Ultra-Violet (ATR-UV) spectroscopy is used as an 'in-situ' measurement technique to measure solute concentration. A modified continuous Mixed Suspension Mixed Product Removal (MSMPR) crystallizer is designed specifically for innovative drug candidates available in limited quantities to derive steady state crystallization kinetics with minimal influence from hydrodynamic phenomena. Batch attrition experiments were carried out to determine the effects of specific power input on the CSD using Lasentec Focussed Beam Reflectance Monitoring (FBRM) to monitor the process on-line and to develop an attrition rate model. Computational Fluid Dynamics (CFD) is a simulation tool that is also introduced to provide valuable insight into mixing, heat transfer and hydrodynamic phenomena within agitated batch cooling suspension crystallization vessels including investigating the effects of scale-up. CFD is used to aid the development of the compartmental modelling framework. The design of the compartmental structure is based on high spatial resolution CFD simulations of internal flow, mixing and heat transfer within crystallizers upon scale-up. The great advantage of using a compartmental modelling framework is that the spatial resolution is reduced and the full population balance with kinetic models can be implemented. The detailed compartmental framework is based on the overall flow pattern, local energy dissipation rate, solids concentration and temperature distribution obtained from CFD. The number, location, cross-sectional area and volume of compartments are determined from CFD results based on the physical crystallizer dimensions. The compartments are selected such that they have approximately uniform temperature, local energy dissipation and solids concentration. Each dynamic compartment has a mass, concentration, enthalpy and population balance combined with MSMPR crystallization kinetic models. The compartments are therefore well mixed and physically connected via interconnecting flows determined from CFD. A general process modelling tool, gPROMS (Process Systems Enterprises) that supports both steady state and dynamics simulations is used to solve sets of ordinary differential and algebraic equations in each compartment. A single compartmental modelling approach is used initially as a first approach without taking into account local variations in process conditions. Predictions on a laboratory scale for an MSMPR and batch cooling crystallizer were satisfactory but upon scale-up the effects of mixing and hydrodynamics is not taken into account and therefore the predictions become less reliable. A compartmentalization approach can be introduced into gPROMS whereby the compartments are modelled as individual units with input and output streams using CFD hydrodynamic information