Sara Badr

Efficient Screening and Selection of Post-combustion CO2 Capture Solvents

We develop an approach for the screening and selection of post combustion CO2 capture solvents using as the performance criteria the molecular and mixture properties associated with thermodynamics, reactivity and sustainability. The proposed approach involves a fast screening stage in which numerous solvents are evaluated based on the simultaneous consideration of pure component properties. Several properties are specifically selected to represent the effects of molecular chemistry on the capture process. A few high-performing solvents are further evaluated using predictive models accounting for the very nonideal mixture behaviour. The prediction of pure component properties is supported by standard group contribution models. The solvent-water-CO2 interactions are represented within the SAFT-VR and SAFT-γ equations of state to predict accurately the mixture vapour-liquid equilibrium behaviour. The proposed developments are tested successfully on a dataset consisting of 126 potential solvent candidates.Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, characterized by irreversible decline of mental faculties, emotional and behavioral changes, loss of motor skills, and dysfunction of autonomic nervous system and disruption of circadian rhythms (CRs). We attempted to describe the morphological findings of the hypothalamus in early cases of AD, focusing our study mostly on the suprachiasmatic nucleus (SCN), the supraoptic nucleus (SON), and the paraventricular nucleus (PVN). Samples were processed for electron microscopy and silver impregnation techniques. The hypothalamic nuclei demonstrated a substantial decrease in the neuronal population, which was particularly prominent in the SCN. Marked abbreviation of dendritic arborization, in association with spinal pathology, was also seen. The SON and PVN demonstrated a substantial number of dystrophic axons and abnormal spines. Alzheimer’s pathology, such as deposits of amyloid-β peptide and neurofibrillary degeneration, was minimal. Electron microscopy revealed mitochondrial alterations in the cell body and the dendritic branches. The morphological alterations of the hypothalamic nuclei in early cases of AD may be related to the gradual alteration of CRs and the instability of autonomic regulation.

Sustainability Assessment of Succinic Acid Production Technologies from Biomass using Metabolic Engineering

Over the past few years, bio-succinic acid from renewable resources has gained increasing attention as a potential bio-derived platform chemical for the detergent/surfactant, ion chelator, food and pharmaceutical markets. Until now, much research was undertaken to lower the production costs of bio-succinic acid, however a multicriteria sustainability evaluation of established and upcoming production processes from a technical perspective is still lacking in the scientific literature. In this study, we combine metabolic engineering with the most mature technologies for the production of bio-succinic acid from sugar beet and lignocellulosic residues. Downstream technologies such as reactive extraction, electrodialysis and ion exchange are investigated together with different upstream technologies such as neutral pH level-, acidic- and high sugar fermentation including metabolically engineered E. coli strains. Different biorefinery concepts are evaluated considering technical, economic, environmental and process hazard aspects in order to gain a broad sustainability perspective of the technologies. The results reveal that energy integration is a key factor for biorefinery concepts in order to be economically reasonable and to achieve lower environmental impacts compared to the conventional production from non-renewable resources. It was found that metabolically engineered E. coli with resistance at the acidic pH level in the fermentation together with reactive extraction in the purification presents the most environmentally competitive technology. However, E. coli strains with resistance at high sugar concentrations together with reactive extraction are revealed to present the most economically competitive technology for the production of bio-succinic acid. Moreover, both technologies are flagged for higher process hazards and require the right measures to enhance process safety and mitigate environmental loads and worker exposure.

Computer-aided molecular design and selection of CO2 capture solvents based on thermodynamics, reactivity and sustainability

The identification of improved carbon dioxide (CO2) capture solvents remains a challenge due to the vast number of potentially-suitable molecules. We propose an optimization-based computer-aided molecular design (CAMD) method to identify and select, from hundreds of thousands of possibilities, a few solvents of optimum performance for CO2 chemisorption processes, as measured by a comprehensive set of criteria. The first stage of the approach involves a fast screening stage where solvent structures are evaluated based on the simultaneous consideration of important pure component properties reflecting thermodynamic, kinetic, and sustainability behaviour. The impact of model uncertainty is considered through a systematic method that employs multiple models for the prediction of performance indices. In the second stage, high-performance solvents are further selected and evaluated using a more detailed thermodynamic model, i.e. the group-contribution statistical associating fluid theory for square well potentials (SAFT-γ SW), to predict accurately the highly non-ideal chemical and phase equilibrium of the solvent–water–CO2 mixtures. The proposed CAMD method is applied to the design of novel molecular structures and to the screening of a data set of commercially available amines. New molecular structures and commercially-available compounds that have received little attention as CO2 capture solvents are successfully identified and assessed using the proposed approach. We recommend that these solvents should be given priority in experimental studies to identify new compounds.

Chapter 11 – Toward Sustainable Solvent-Based Postcombustion CO2 Capture: From Molecules to Conceptual Flowsheet Design

Solvent-based postcombustion carbon dioxide (CO2) capture requires minimum retrofitting of current CO2-emitting power plants but is challenging because of the high energy penalty in solvent regeneration and the environmental impacts of solvent degradation. Research efforts are predominantly based on lab and pilot-scale experiments to select solvents and process systems which improve the overall performance of this technology. Notwithstanding the value of the experimental efforts, this study proposes an efficient computational approach for screening a vast number of commercial and novel solvents and process configurations. Computer-aided molecular design, advanced group contribution methods, process synthesis, and multicriteria sustainability assessment are combined to provide new insights in solvent-based CO2 capture. This study provides details of the data requirements, highlights several high-performance solvents and process configurations, and quantifies the benefits from economic, life cycle, and hazard assessment perspective. Thus, it also provides information for the experimental approaches, focusing on a narrower, near-optimum design space.

Model-based Analysis of Waste Management Systems through a Natural Language Approach

Abstract Waste management systems are complex interactive networks of data, knowledge and models comprising multiple parameters. In these systems the technologies and their models interact with each other in order to fully describe the problem. Inputs and outputs of the models intersect depending on the various parameters. These parameters, such as composition, pH, temperature etc., need to be computed simultaneously in order to select the appropriate processing path prior to model development. Therefore, the the selected path and the waste stream characteristics determine the context of the model. Waste management systems involve non-experts in decisions that require expertise from many disciplines. The need to synthesize the multidisciplinary domains of waste, integrate the knowledge and express them with common natural language terms is outlined by this work. The work builds on recent technology that was produced to synthesize superstructures for integrated biorefineries (Magioglou et al. 2014), which now extends to decision-making and allocation problems for waste management systems. The approach employs ontologies, Java language, OpenBabel library and suggests a natural language framework. Illustrations include an industrial wastewater treatment system of the chemical sector which evaluates the environmental impacts. The framework successfully synthesizes the network and allocates the waste, technologies, streams and auxiliaries along with their parameters.