High demand and consumption rates of ecological materials and services to satisfy societal needs and for the dissipation of emissions are quickly exceeding the capacity that nature can provide. To avoid a tipping point situation, where ecological services may no longer be available, society must consider a sustainable path forward. The chemical industry’s response is to incorporate a sustainability approach early into process design to reduce the quantity of goods and services needed and to prevent and minimize releases, while increasing their economic and social benefits. This approach leads to design modifications of existing and new chemical processes, which requires a complete sustainability performance assessment that can support a decision-maker to determine whether a process is becoming more or less sustainable. Hence, the development of indicators capable of assessing process sustainability becomes crucial. This work presents a taxonomic classification and definition of sustainability indicators according to the environmental, efficiency, energy, and economic bases proposed by the GREENSCOPE methodology for the evaluation and design of sustainable processes. In addition, this work proposes a general scale for measuring sustainability according to the identification and use of best possible target and worst-case scenarios as reference states, as the upper and lower bounds of a sustainability measurement scale. This taxonomy will prove valuable in evaluating chemical process sustainability in the various stages of design and optimization.

Na + -SAPO-34 sorbents were ion-exchanged with several individual metal cations to study their effect on the adsorption of similar size light gases. Measurements of pure component adsorption equilibria, with emphasis on CO 2 , were performed at different temperatures (273-348 K) and pressures (<1 atm). Adsorption isotherms for CO 2 in M n+ -SAPO-34 materials displayed a nonlinear behavior and did not follow the typical pore-filling mechanism. In general, the overall adsorption performance of the exchanged materials increased as follows:

Demands for quick and accurate life cycle assessments create a need for methods to rapidly generate reliable life cycle inventories (LCI). Data mining is a suitable tool for this purpose, especially given the large amount of available governmental data. These data are typically applied to LCIs on a case-by-case basis. As linked open data becomes more prevalent, it may be possible to automate LCI using data mining by establishing a reproducible approach for identifying, extracting, and processing the data. This work proposes a method for standardizing and eventually automating the discovery and use of publicly available data at the United States Environmental Protection Agency for chemical-manufacturing LCI. The method is developed using a case study of acetic acid. The data quality and gap analyses for the generated inventory found that the selected data sources can provide information with equal or better reliability and representativeness on air, water, hazardous waste, on-site energy usage, and production volumes but with key data gaps including material inputs, water usage, purchased electricity, and transportation requirements. A comparison of the generated LCI with existing data revealed that the data mining inventory is in reasonable agreement with existing data and may provide a more-comprehensive inventory of air emissions and water discharges. The case study highlighted challenges for current data management practices that must be overcome to successfully automate the method using semantic technology. Benefits of the method are that the openly available data can be compiled in a standardized and transparent approach that supports potential automation with flexibility to incorporate new data sources as needed.

To begin repair of the environmental quality of the planet, there is a need to embrace sustainable development at many levels of the chemical industry and society. One such manner in which the chemical industry is responding to this need is through sustainability evaluations, retrofits, and new process designs. For improving the sustainability of chemical processes, the first contribution of this set of articles presented a taxonomic classification, definition, and scale for measuring sustainability indicators according to the GREENSCOPE (Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator) methodology. To generate a sustainability assessment and yield a more sustainable process in the end, a model must have available data; that is, data are not the final goal but are mandatory to arrive there. This second contribution extends this methodology by identifying data requirements when calculating the indicators. Each indicator is mathematically defined, emphasizing realistic usage, and connecting the mathematical formulas for the indicators with their data requirements. In addition, data-source alternatives to fulfill the input requirements and potential data gaps for the calculation of the sustainability indicators for a new process technology or an existing manufacturing plant are proposed and discussed. This work further provides the practical connection between theoretical definitions of sustainability, data needs, and mathematical definitions of indicators for process sustainability assessment. With the accomplishment of this second contribution, sustainability assessment can be achieved and proposed as a reliable and robust tool for the development and optimization of chemical processes.

A methodology is described for developing a gate-to-gate life cycle inventory (LCI) of a chemical manufacturing process to support the application of life cycle assessment in the design and regulation of sustainable chemicals. The inventories were derived by first applying process design and simulation to develop a process flow diagram describing the energy and basic material flows of the system. Additional techniques developed by the United States Environmental Protection Agency for estimating uncontrolled emissions from chemical processing equipment were then applied to obtain a detailed emission profile for the process. Finally, land use for the process was estimated using a simple sizing model. The methodology was applied to a case study of acetic acid production based on the Cativa process. The results reveal improvements in the qualitative LCI for acetic acid production compared to commonly used databases and top-down methodologies. The modeling techniques improve the quantitative LCI results for inputs and uncontrolled emissions. With provisions for applying appropriate emission controls, the proposed method can provide an estimate of the LCI that can be used for subsequent life cycle assessments.