Stavros Papadokonstantakis

Bridging data gaps in environmental assessments: Modeling impacts of fine and basic chemical production

The chemical industry is increasing its efforts to reduce the environmental burdens of chemical production. One focus is to implement energy-efficient processes and green technologies early in the process design to maximize environmental efficiency and to reduce costs. However, as data on many chemical products are scarce, many sustainability studies are hampered by the lack of information on production processes, and chemicals are often neglected or only crudely estimated. Models that estimate production data and environmental burdens can be vital tools to aid sustainability efforts. In addition, they are useful for the environmental assessment of chemicals without access to production data, i.e. in supply-chain management or for the assessment of products using chemicals as materials. Using mass and energy flow data on the petrochemical production of 338 chemicals, we developed models that can estimate key production parameters directly from the molecular structure. The data sources were mostly production data provided by industrial partners, extended by data from the ecoinvent database. The predicted parameters were the Cumulative Energy Demand (CED), the Global Warming Potential (GWP), the Eco-indicator 99 score, a Life Cycle Assessment (LCA) method, and the electricity and heat use over the production cycle. Model outputs include a measure of the prediction uncertainty. The median relative errors of the models were between 10% and 30%, within acceptable ranges for estimations. The modelled parameters offer a thorough insight into the environmental performance of a production process and the model estimates can be of great service in process design, supply-chain management and environmental assessments of chemical products in the early planning and design stages where production data are not available.

Environmental and economic assessment of lactic acid production from glycerol using cascade bio- and chemocatalysis

Recently, lactic acid has emerged as one of the most relevant platform molecules for the preparation of bio-chemicals. Due to the limited productivity of sugar fermentation, the dominant industrial technology practiced for its manufacture, new chemocatalytic processes are being developed in order to meet the expected demand for this intermediate. The Lewis-acid catalysed isomerisation of dihydroxyacetone has attracted particular interest. If the reaction is performed in water, lactic acid is attained directly, while if alcohol is used as the solvent, the desired product can be obtained upon subsequent hydrolysis of the alkyl lactates formed. Herein, we (i) demonstrate tin-containing MFI zeolites prepared by scalable methods as highly active, selective and recyclable catalysts able to operate in concentrated dihydroxyacetone aqueous and methanolic solutions, and (ii) reveal by life cycle analysis that a process comprising the enzymatic production of dihydroxyacetone from crude glycerol and its chemocatalytic isomerisation in methanol is advantageous for the production of lactic acid compared to glucose fermentation in terms of both sustainability and operating costs. In particular, we demonstrate that the reduced energy requirements and CO2 emissions of the cascade process originate from the valorisation of a waste feedstock and from the high performance and recyclability of the zeolite catalyst and that the economic advantage is strongly determined by the comparably low market price of glycerol. It is also shown that the bio-/chemocatalytic route remains ecologically and economically more attractive even if the purity of glycerol is as low as 38%.

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.

Multi-objective optimization of industrial waste management in chemical sites coupled with heat integration issues

Abstract This work presents a multi-period waste management multi-objective optimization, considering economic and environmental issues. The behavior of waste treatment units is included in the optimization problem as black-box models based on industrial practice. A multi-objective mathematical strategy based on the normalized constrained method is applied. An industrial based case study is analyzed. The proposed rigorous multi-objective optimization leads to reduced computation effort and better solutions in terms of solution quality, since waste stream scheduling has been included in decision-making. In addition, a sequential approach is followed to further estimate the minimum heat requirements for the different solutions obtained in the Pareto front using a MILP formulation of the heat exchange problem. Hot and cold sink requirements can be reduced by 80% and 99% respectively.

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.

Integrated waste management in batch chemical industry based on multi-objective optimization

A multi-objective optimization methodology for hazardous liquid waste management is presented in this paper using industrially based LCA models and operating constraints. This approach is used to optimize the handling of waste streams introducing flexible mixing policy scenarios compared to the rigid policy scenarios of the industrial system. It is shown that increasing the degrees of freedom for the waste mixing reduces significantly both the operating cost and the environmental impact by avoiding the use of utilities. Moreover, the influence of waste availability as function of production planning without waste storage is analyzed in several multiperiod optimizations. There, it is demonstrated that this saving potential can be further increased by integration of multiperiod production planning with waste management policies, up to the level of 40% for the environmental impact, and more than 50% for the operating cost, compared to the industrial base case. In some specific cases, a proper matching of production planning and waste mixing policies can also turn the waste treatment into a source of profit exploiting energy production from the incineration process. Implications This study reveals the savings potential of more flexible policies in waste management, in particular waste mixing of liquid waste in batch chemical industries treated in incineration, wet air oxidation, wastewater treatment plants, or recovered by distillation. Through a multi-objective optimization framework including models for operating costs and life-cycle inventories based on industrial data, operating constraints from industrial practice, and terminal constraints from legislation, savings potentials up to 50% for the operation cost and 40% for the environmental impact are demonstrated in two case studies. Supplemental Materials: Supplemental materials are available for this paper. Go to the publishers online edition of the Journal of the Air & Waste Management Association.

Investigating the use of path flow indicators as optimization drivers in batch process retrofitting

Abstract Improvement of batch processes has gained much attention in industry due to the competitive market and stricter environmental legislation. This paper focuses on a process flowsheet decomposition based methodology resulting in path flow indicators which are able to highlight process alternatives with an improved performance. The novel aspects introduced in this methodology are the implementation of a new path flow indicator category that focuses on unit occupancy time and the multi-objective process assessment in order to reveal sustainable retrofitting actions. Furthermore, the retrofit alternatives are not only classified according to the diverse objective functions but also to the differences observed in the path flow indicator matrix of the generated retrofit alternatives compared to the base case. This classification enables a more detailed analysis of the retrofit alternative impact and illustrates the potential of path flow indicators as optimization drivers. The methodology is exemplified in an industrial batch process case study.

Multi-criteria screening of chemicals considering thermodynamic and life cycle assessment metrics via data envelopment analysis: application to CO2 capture

With the growing trend of incorporating sustainability principles in the chemical industry, there is a clear need to develop decision-making tools to quantify and optimise the sustainability level of chemical products and processes. In this study, we propose a systematic approach based on Data Envelopment Analysis (DEA) for the multi-criteria screening of molecules according to techno-economic and environmental aspects. The main advantage of our method is that it does not require any articulation of preferences via subjective weighting of the assessment criteria. Furthermore, our approach identifies the most efficient chemicals (according to some sustainability criteria) and for the ones found to be inefficient it establishes in turn improvement targets that can be used to guide research efforts in green chemistry. Our method was applied to the screening of 125 amine-based solvents for CO2 capture considering 10 different performance indicators, which are relevant to technical, health, safety and environmental aspects, including CO2 solubility, molar volume, surface tension, heat capacity, viscosity, vapour pressure, mobility, fire & explosion, acute toxicity and Eco-indicator 99. Our approach eliminates 36% of the solvents (as they are found to be inefficient), identifies the main sources of inefficiency (e.g., properties displaying poor values that should be improved) and ranks the best chemicals according to an objective criterion that does not rely on weights. Overall, our proposed DEA-based framework offers insightful guidance to make chemicals more sustainable.