Digitalization in Thermodynamics

Digitalization is about data and how they are used. This has always been a key topic in applied thermodynamics. In the present work, the influence of the current wave of digitalization on thermodynamics is analyzed. Thermodynamic modeling and simulation is changing as large amounts of data of different nature and quality become easily available. The power and complexity of thermodynamic models and simulation techniques is rapidly increasing, and new routes become viable to link them to the data. Machine learning opens new perspectives, when it is suitably combined with classical thermodynamic theory. Illustrated by examples, different aspects of digitalization in thermodynamics are discussed: strengths and weaknesses as well as opportunities and threats

Influence of Salts on the Adsorption of Lysozyme on a Mixed-Mode Resin

Mixed-mode chromatography (MMC), which combines features of ion exchange chromatography (IEC) and hydrophobic interaction chromatography (HIC), is an interesting method for protein separation and purification. The design of MMC processes is challenging as adsorption equilibria are influenced by many parameters, including ionic strength and the presence of different salts in solution. Systematic studies on the influence of those parameters in MMC are rare. Therefore, in the present work, the influence of four salts, namely, sodium chloride, sodium sulfate, ammonium chloride, and ammonium sulfate, on the adsorption of lysozyme on the mixed-mode resin Toyopearl MX-Trp-650M at pH 7.0 and 25°C was studied systematically in equilibrium adsorption experiments for ionic strengths between 0 mM and 3000 mM. For all salts, a noticeable adsorption strength was observed over the entire range of studied ionic strengths. An exponential decay of the loading of the resin with increasing ionic strength was found until approx. 1000 mM. For higher ionic strengths, the loading was found to be practically independent of the ionic strength. At constant ionic strength, the highest lysozyme loadings were observed for ammonium sulfate, the lowest for sodium chloride. A mathematical model was developed that correctly describes the influence of the ionic strength as well as the influence of the studied salts. The model is the first that enables the prediction of adsorption isotherms of proteins on mixed-mode resins in a wide range of technically interesting conditions, accounting for the influence of the ionic strength and four salts of practical relevance

Perspective: Machine Learning of Thermophysical Properties

In this first contribution to Fluid Phase Equilibria’s Perspective Series, we discuss the role of machine learning (ML) in research on thermophysical properties. Following the idea behind the new series, this is no classical review aiming at a comprehensive coverage of previous work on the field. Instead, we provide an overview of the developments and point out promising new directions in the field, linking the perspectives of chemical engineers and computer scientists. The topics we cover include the role of data in research on thermophysical properties; the long history of ML methods in this field, which, however, stemmed so far almost exclusively from supervised learning; other ML methods of interest; as well as the important subject of how to merge physical modeling with ML to create hybrid approaches, which we expect to play a central role in the future. The discussion is illustrated by examples of the application of matrix completion methods from ML for the prediction of mixture properties, which we have recently introduced

NEAT-NMR Spectroscopy for the Estimation of Activity Coefficients of Target Components in Poorly Specified Mixtures

Mixtures of which the composition is not fully known are important in many fields of engineering and science, for example, in biotechnology. Owing to the lack of information on the composition, such mixtures cannot be described with common thermodynamic models. In the present work, a method is described with which this obstacle can be overcome for an important class of problems. The method enables the estimation of the activity coefficients of target components in poorly specified mixtures and is based on a combination of NMR spectroscopy with a thermodynamic group contribution method. It is therefore called the NEAT method (NMR spectroscopy for the Estimation of Activity coefficients of Target components in poorly specified mixtures). In NEAT, NMR spectroscopy is used to obtain information on the concentrations of chemical groups in the mixture. The elucidation of the speciation is not required, only the target component has to be known. Modified UNIFAC (Dortmund) is applied in the present work as group contribution method, but NEAT can be extended to any other group contribution method. NEAT was introduced recently by our group in a short communication, but only the basic ideas were presented. In the present work, NEAT is described in full detail. Different options of using NEAT are discussed, and examples for the application of the method are given. They include a variety of aqueous and nonaqueous mixtures. The results show very good agreement of the activity coefficients that are predicted by NEAT with the corresponding results for the fully specified mixtures

Application of NEAT for Determining the Composition Dependence of Activity Coefficients in Poorly Specified Mixtures

Poorly specified mixtures, of which the composition is only partially known, are important in many fields. In a recent work of our group, the method NEAT for estimating activity coefficients of target components in such mixtures was introduced. In the present work, it is shown that based on a single NMR analysis of a poorly specified mixture not only the activity coefficient of the target component in that mixture but also its composition dependence can be predicted with NEAT. Hence, based on a single NMR analysis, the activity coefficient of the target component in many mixtures can be predicted with NEAT. This is possible, if the unknown part of the mixture remains unchanged, which is the case e.g. when the target component is selectively removed or when a known solvent is added or removed. The approach is successfully tested using several aqueous test mixtures

Influence of Salts on the Adsorption of Lysozyme on a Mixed-Mode Resin

Mixed-mode chromatography (MMC), which combines features of ion exchange chromatography (IEC) and hydrophobic interaction chromatography (HIC), is an interesting method for protein separation and purification. The design of MMC processes is challenging as adsorption equilibria are influenced by many parameters, including ionic strength and the presence of different salts in solution. Systematic studies on the influence of those parameters in MMC are rare. Therefore, in the present work, the influence of four salts, namely, sodium chloride, sodium sulfate, ammonium chloride, and ammonium sulfate, on the adsorption of lysozyme on the mixed-mode resin Toyopearl MX-Trp-650M at pH 7.0 and 25°C was studied systematically in equilibrium adsorption experiments for ionic strengths between 0 mM and 3000 mM. For all salts, a noticeable adsorption strength was observed over the entire range of studied ionic strengths. An exponential decay of the loading of the resin with increasing ionic strength was found until approx. 1000 mM. For higher ionic strengths, the loading was found to be practically independent of the ionic strength. At constant ionic strength, the highest lysozyme loadings were observed for ammonium sulfate, the lowest for sodium chloride. A mathematical model was developed that correctly describes the influence of the ionic strength as well as the influence of the studied salts. The model is the first that enables the prediction of adsorption isotherms of proteins on mixed-mode resins in a wide range of technically interesting conditions, accounting for the influence of the ionic strength and four salts of practical relevance

Application of NEAT for the Simulation of Liquid-liquid Extraction Processes with Poorly Specified Feeds

The conceptual design of fluid separation processes is particularly challenging if the considered mixtures are poorly specified, since classical thermodynamic models cannot be applied when the composition is unknown. We have recently developed a method (NEAT) to predict activity coefficients in such mixtures. It combines the thermodynamic group contribution concept with the ability of NMR spectroscopy to quantify chemical groups. In the present work, we describe how NEAT can be applied to equilibrium stage simulations of liquid–liquid extraction processes with poorly specified feeds. Only a single 13C NMR spectrum of the feed is needed for predicting the distribution of a target component for different process parameters, such as temperature or extracting agent. The predictions from several test cases are compared to results that are obtained using the full knowledge on the composition of the feed and surprisingly good agreement is found

Solid-liquid Equilibrium in the System 2-Keto-L-gulonic Acid + Sodium-2-keto-L-gulonate + Water

The solid-liquid equilibrium (SLE) in the ternary system 2-keto-L-gulonic acid (HKGA) + sodium-2-keto-L-gulonate (NaKGA) + water was studied experimentally at temperatures between 275 and 313 K and ambient pressure. At these conditions, HKGA and NaKGA precipitate as monohydrates: HKGA H2O and NaKGA H2O, respectively. Phase diagrams with one eutonic point are found for all temperatures. A thermodynamic model of the SLE that is based on an extended version of the Debye-Hückel theory was developed and the dissociation constant of HKGA as well as the solubility products of HKGA H2O and NaKGA H2O were determined. The agreement between the experimental data and the results from the model is excellent

Influence of Salts on the Adsorption of Lysozyme on a Mixed-Mode Resin

Mixed-mode chromatography (MMC), which combines features of ion exchange chromatography (IEC) and hydrophobic interaction chromatography (HIC), is an interesting method for protein separation and purification. The design of MMC processes is challenging as adsorption equilibria are influenced by many parameters, including ionic strength and the presence of different salts in solution. Systematic studies on the influence of those parameters in MMC are rare. Therefore, in the present work, the influence of four salts, namely, sodium chloride, sodium sulfate, ammonium chloride, and ammonium sulfate, on the adsorption of lysozyme on the mixed-mode resin Toyopearl MX-Trp-650M at pH 7.0 and 25°C was studied systematically in equilibrium adsorption experiments for ionic strengths between 0 mM and 3000 mM. For all salts, a noticeable adsorption strength was observed over the entire range of studied ionic strengths. An exponential decay of the loading of the resin with increasing ionic strength was found until approx. 1000 mM. For higher ionic strengths, the loading was found to be practically independent of the ionic strength. At constant ionic strength, the highest lysozyme loadings were observed for ammonium sulfate, the lowest for sodium chloride. A mathematical model was developed that correctly describes the influence of the ionic strength as well as the influence of the studied salts. The model is the first that enables the prediction of adsorption isotherms of proteins on mixed-mode resins in a wide range of technically interesting conditions, accounting for the influence of the ionic strength and four salts of practical relevance

Method for Estimating Activity Coefficients of Target Components in Poorly Specified Mixtures

Mixtures that contain a known target component but are otherwise poorly specified 15 are important in many fields. Previously, the activity of the target component, which is needed e.g. to design separation processes, could not be predicted in such mixtures. A method was developed to solve this problem. It combines a thermodynamic group contribution method for the activity coefficient with NMR spectroscopy, which is used for estimating the nature and amount of the different chemical groups in the mixture. The knowledge of the component 20 speciation of the mixture is not required. Test cases that are inspired by bioprocess engineering applications show that the new method gives surprisingly good results