Incorporating uncertainty in chemical process design for environmental risk assessment (original) (raw)
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Quantifying uncertainty in chemical systems modeling
International Journal of Chemical Kinetics, 2005
This study compares two techniques for uncertainty quantification in chemistry computations, one based on sensitivity analysis and error-propagation, and the other on stochastic analysis using polynomial chaos techniques. The two constructions are studied in the context of H 2 -O 2 ignition under supercritical-water conditions. They are compared in terms of their prediction of uncertainty in species concentrations and the sensitivity of selected species concentrations to given parameters. The formulation is extended to 1-D reacting-flow simulations. The computations are used to study sensitivities to both reaction rate preexponentials and enthalpies, and to examine how this information must be evaluated in light of known, inherent parametric uncertainties in simulation parameters. The results indicate that polynomial chaos methods provide similar firstorder information to conventional sensitivity analysis, while preserving higher-order information that is needed for accurate uncertainty quantification and for assigning confidence intervals on sensitivity coefficients. These higher-order effects can be significant, as the analysis reveals substantial uncertainties in the sensitivity coefficients themselves.
Uncertainty in chemical process systems engineering: a critical review
Reviews in Chemical Engineering, 2019
Uncertainty or error occurs as a result of a lack or misuse of knowledge about specific topics or situations. In this review, we recall the differences between error and uncertainty briefly, first, and then their probable sources. Then, their identifications and management in chemical process design, optimization, control, and fault detection and diagnosis are illustrated. Furthermore, because of the large amount of information that can be obtained in modern plants, accurate analysis and evaluation of those pieces of information have undeniable effects on the uncertainty in the system. Moreover, the origins of uncertainty and error in simulation and modeling are also presented. We show that in a multidisciplinary modeling approach, every single step can be a potential source of uncertainty, which can merge into each other and generate unreliable results. In addition, some uncertainty analysis and evaluation methods are briefly presented. Finally, guidelines for future research are proposed based on existing research gaps, which we believe will pave the way to innovative process designs based on more reliable, efficient, and feasible optimum planning.
Competition requires companies to make decisions that satisfy multiple criteria. Considering profitability alone is no longer sufficient. Ignoring environmental considerations will not only expose a company to potential regulatory costs, but also damaged public image, both of which in turn have negative effect on the economic well-being of companies. At the same time, the fast changing business environment requires companies to reach decisions in a speedy fashion. This work describes a decision-making framework that addresses the obstacles in integrating environmental considerations into technology selections with focus on the semiconductor and flat panel industry. It addresses data availability and data quality issues in environmental evaluations through the uncertainty analysis. It tackles the mismatch between the short innovation cycles in the industry and the long environmental analysis time by a combination of the uncertainty analysis, non-linear sensitivity analysis, hierarchical modeling, and the value of information analysis. It bridges the gap between environmental evaluations, economical evaluations, and technical evaluations by a unified modeling platform that links the process model, the cost-of-ownership model, and the environmental valuation model along with the databases and the random number generators for the uncertainty analysis. It is a generic framework and can be applied to various decision scenarios that face uncertainty in their systems. The paper also reviews sensitivity analysis methods and includes a survey on the current status and needs on environmental, safety, and health in the industry. A case study on Cu CVD illustrates the methods of the evaluations models. A case study on comparing NF3 and F2 as the chamber cleaning gas illustrates the decision-making framework.
Decision support tools for environmentally benign process design under uncertainty
Computers & Chemical Engineering, 2004
This paper presents a new systematic framework for the synthesis of an environmentally benign process under uncertainty. The uncertainty is classified depending on its sources and mathematical model structure as deterministic or stochastic. The proposed methodology is a two-layer algorithm. In the outer layer, the synthesis problem is represented by a multi-objective optimization problem considering the performances associated with design parameters. In the inner layer, the problem is expressed as a single-objective optimization problem taking in to account the operating performances in the presence of uncertainty. The proposed hybrid approach consisting of multi-period and stochastic optimization formulations is then employed. Additionally, the effect of variability on decisions related to process performance and quality is also discussed. Applicability of the developed methodology is illustrated in a case study. Four configurations of membrane-based toluene recovery process are investigated to quantitatively compare economic, environmental and robustness performance of each configuration. The developed methodology can select solutions with minimal environmental impacts and adequate robustness at a desired economic performance.
Computer Aided Chemical Engineering, 2007
The concept of sustainable development is a common one in the 21 st century. The three important elements of the sustainability are: economy, society and environment. The chemical processes, which are directly connected with the economy, affect in different ways the environment and the society. The Waste Reduction Algorithm (WAR Algorithm), the methodology developed by Cabezas with the goal of determining the potential environmental impact (PEI) of a chemical process, is used in this work as a decision support tool to choose the best environmental solution from different alternative design for a chemical process. The equations of the WAR Algorithm have been implemented in a standard computer code, CAPE OPEN, and the code was tested in different process simulators. One possible limitation for the applicability of WAR Algorithm to a given process is the availability of toxicological data. At present the Environmental Protection Agency (EPA) has a databank of toxicological properties that covers 1707 compounds that may not be enough for some particular process. In this paper we present a methodology for predicating the toxicological data in the form compatible with the EPA data bank, from molecular modelling. Several different approaches are used for the estimation of the thermo-physical properties including quantum chemistry, molecular dynamics, Monte Carlo and QSAR. 2 M. Fermeglia et al.
Process Safety and Environmental Protection, 2017
Fugitive emissions are one of the most notable contributors to atmospheric releases in chemical process industries affecting not only the economy but also both the environment and workers' health. In order to reduce fugitive emissions released in this process, the assessment of fugitive emissions should be conducted at the early stage of chemical process design when the cost of making changes is the lowest. This is however impeded by the limited knowledge on the leak sources, since the process is still under design. The paper presents a hybrid approach for estimating fugitive emission rates early in the design phase. The method combines a pre-calculated emission database of standard process module based approach with estimation by using generic piping and instrumentation diagrams if a suitable module is not available from the database. Also, both working and breathing emissions are included in the assessment. The emissions' assessment from a case study of hydrodealkylation distillation and storage systems is presented to demonstrate the proposed approach.
2003
The air quality modeling community widely acknowledges that there are considerable uncertainties in the emission inventories used for modeling. Nonetheless, these uncertainties are typically ignored in air quality modeling applications. Three reasons for ignoring these uncertainties are (1) uncertainties in model inputs typically are not well quantified, (2) emissions and air quality modeling codes are not equipped to support uncertain inputs, and (3) methodologies are lacking that address the previous two issues. In this paper, we describe a methodology for integrating some types of emissions uncertainties into emissions modeling. The methodology is applied to an air quality modeling application, allowing the effects of emissions uncertainties on air quality simulations to be characterized. To facilitate this application, the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system has been modified to accept statistical or empirical distributions describing emissions factors as well as multiple realizations of hour-specific emissions data. SMOKE was used with these data to simulate many alternative realizations of the emissions inventory. These realizations were then modeled with the Multiscale Air Quality Simulation Platform (MAQSIP) and the uncertainty in the air quality model predictions was characterized. Computational intensity is addressed by distributing the model runs over a cluster of inexpensive Linux computers. The paper concludes with recommendations for improving the characterization of uncertainty in emissions inventories, with the goal of making probabilistic assessments more practical.
Estimation of chemical concentration due to fugitive emissions during chemical process design
Process Safety and Environmental Protection, 2010
2010. Estimation of chemical concentration due to fugitive emissions during chemical process design. Process Safety and Environmental Protection, volume 88, number 3, pages 173-184. a b s t r a c t Fugitive emissions are not an environmental concern alone, but are also a health concern. From occupational health standpoint, fugitive emissions are the main sources of origin of the continuous exposure to workers. Operating plants regularly measure release and concentration levels through a plant-monitoring program. However, for processes which are still 'on paper', predictive estimation methods are required. Therefore, three methods for estimating concentration of the fugitive emissions are presented for the process development and design phases of petrochemical processes. The methods estimate the fugitive emission rates and plant plot dimensions resulting to fugitive emission concentrations. The methods were developed for the type and amount of information available in three process design stages; conceptual design, preliminary process design, and detailed process design. The methods are applied on a real benzene plant; the estimated benzene concentrations are compared to the actual concentration measured at the plant. The results show that as the information mounts up during design, the concentration estimate becomes more accurate. The results indicate that the methods presented provide simple estimates of fugitive emission-based concentrations during the design stages.
Environmental Modelling & Software, 2008
This paper presents a set of tools developed to support an innovative methodology to design and upgrade wastewater treatment systems in a probabilistic way. For the first step, data reconstruction, two different tools were developed, one for situations where data are available and another one where no data are available. The second step, modelling and simulation, implied the development of a new simulation platform and of distributed computation software to deal with the simulation load generated by the third step, uncertainty analysis, with Monte Carlo simulations of the system over one year, important dynamics and stiff behaviour. For the fourth step, evaluation of alternatives, the evaluator tool processes the results of the simulations and plots the relevant information regarding the robustness of the process against input and parameters uncertainties, as well as concentrationeduration curves for the risk of non-compliance with effluent and receiving water quality limits. This paper illustrates the merits of these tools to make the innovative methodology of practical interest. The design practice should move from conventional procedures suited for the relatively fixed context of emission limits, to more advanced, transparent and cost-effective procedures appropriate to cope with the flexibility and complexity introduced by integrated water management approaches.