Brian Veitch

Life cycle iNdeX (LInX): a new indexing procedure for process and product design and decision-making

Life cycle assessment (LCA) is an important technique in the successful implementation of a process or product development in the context of environmental sustainability. Attempts have been made to incorporate LCA in public and corporate processes and product related decision-making. The European Union’s eco-labeling schemes and the United Kingdom’s Integrated Pollution Prevention and Control Directive have tried to integrate life cycle thinking with policy making. However, these efforts still have not made LCA an integral part of process and product selection and design. The absence of an easy to use tool for rapid reconnaissance is a basic limitation of the LCA application. A new life cycle indexing system — LInX — is proposed, which will facilitate the LCA application in process and product evaluation and decision-making. The LInX is comprised of four important sub-indices or attributes — environment, health and safety (EHS), cost, technical feasibility, and socio-political factors. Further, each attribute contains a number of basic parameters, e.g. EHS consists of 11 parameters. Quantification of each basic parameter is performed for the complete life cycle of a proposed process or product. An analytical hierarchy process is used to compute the weights for each basic parameter and sub-indices. A composite process is used to determine the final overall index. This paper explains the methodology for computation of the new indexing system and demonstrates it with an application.

Fault and Event Tree Analyses for Process Systems Risk Analysis: Uncertainty Handling Formulations

Quantitative risk analysis (QRA) is a systematic approach for evaluating likelihood, consequences, and risk of adverse events. QRA based on event (ETA) and fault tree analyses (FTA) employs two basic assumptions. The first assumption is related to likelihood values of input events, and the second assumption is regarding interdependence among the events (for ETA) or basic events (for FTA). Traditionally, FTA and ETA both use crisp probabilities; however, to deal with uncertainties, the probability distributions of input event likelihoods are assumed. These probability distributions are often hard to come by and even if available, they are subject to incompleteness (partial ignorance) and imprecision. Furthermore, both FTA and ETA assume that events (or basic events) are independent. In practice, these two assumptions are often unrealistic. This article focuses on handling uncertainty in a QRA framework of a process system. Fuzzy set theory and evidence theory are used to describe the uncertainties in the input event likelihoods. A method based on a dependency coefficient is used to express interdependencies of events (or basic events) in ETA and FTA. To demonstrate the approach, two case studies are discussed.

The life story of a first-year sea ice ridge

Abstract Field data are described and analyzed from all-winter monitoring of the structure and temperature of one sea ice ridge in the northern Baltic Sea in the winter of 1991. The ridge aged to 3.5 months and experienced substantial structural evolution: the consolidated layer grew to 1 m, average porosity decreased from 0.28 to 0.18, keel thickness decreased by 1 m, and the ridge geometry became smoother. The porosity decreased due to freezing and showed a persistent minimum of 0.20–0.23 in the midkeel region; the void distribution changed due to packing rearrangements of ice blocks. Ice volume changed due to thermodynamic growth and decay. Within the sail and consolidated layer the heat flow was mainly vertical varying with time according to the surface forcing; the corresponding total ice production estimated from the temperature data would be 0.14 m, a bit more than the measured ice production 0.10 m. Predictions of the consolidated layer growth, based on (a) ice surface temperature or (b) air temperature or (c) local undeformed ice growth, gave good results. In spring the ice blocks throughout the keel beneath the consolidated layer melted uniformly.

Distribution of heavy metals in sediment pore water due to offshore discharges: an ecological risk assessment

Barite is used as a weighting agent in synthetic and oil based drilling fluids (SBFs and OBFs) to maintain bore hole pressure during offshore oil drilling operations. Substitution of OBFs by SBFs has reduced the risk of ecological impacts. Barite makes up approximately 33% by weight of an SBF and contains traces of heavy metals, which significantly contribute to the toxicity of drilling waste. Due to the hydrophobic nature of SBFs, drilling wastes are not dispersive in the water column. Arsenic, copper and lead are the three important toxic heavy metals, amongst others, found in the drilling waste. The concentrations of heavy metals are determined using a steady state aquivalence-based fate model in a probabilistic mode. Monte Carlo (MC) simulations using Latin Hypercube Sampling (LHS) are employed to determine pore water concentrations at known pollutant loading rates (E) and impact area (AW) conditions. This paper considers a hypothetical case study to determine the water quality impacts for 4 and 10% attached SBFs which correspond to proposed best available technology (BAT) option and current discharge practice in US offshore. The exposure concentration (CE) is a predicted environmental concentration (PEC), which is adjusted for exposure probability (p) and bioavailable fraction (BF) of heavy metals. The probabilistic response of an ecosystem is defined by developing an empirical distribution function (EDF) of predicted-no-effect-concentration (PNEC) derived from LC50 and NOEC data. The lowest 10th percentile value of an EDF is defined as a representative response of an ecosystem, RE. The ratio of exposure concentration (CE) to the representative response (RE) is defined risk quotient (RQ). The ratio CE/RE 1).

Developing a Quantitative Tool for Sustainability Assessment of HEIs.

Purpose – Implementation of a sustainability paradigm demands new choices and innovative ways of thinking. The main objective of this paper is to provide a meaningful sustainability assessment tool for make informed decisions, which is applied to higher education institutions (HEIs).Design/methodology/approach – The objective is achieved by developing a quantitative tool for sustainability assessment using a driving force‐pressure‐state‐exposure‐effect‐action (DPSEEA) framework. The DPSEEA framework considers environmental, social, economic, and educational performance as main dimensions of sustainability. The proposed model is called DPSEEA‐Sustainability index Model (D‐SiM). The D‐SiM is a causality‐based model in which the sustainability index (SI) is an outcome of nonlinear effects of sustainability indicators in various stages of DPSEEA. To have an improved understanding of input factors (driving forces) and their impact on sustainability, a simplified empirical model is developed and applied to HEIs...

A model for estimating the probability of missile impact: Missiles originating from bursting horizontal cylindrical vessels

Past explosion events in process facilities have centered attention on the fact that missiles generated as the result of vessel fragmentation pose significant risk to personnel and process equipment and can trigger knock‐on or domino effects in industrial accidents. To promote the design of inherently safer facilities—and to enable more effective mitigation and control measures with respect to missile risks—it is necessary to perform missile risk analysis studies at the early design phase. To aid in such an analysis and to predict domino scenarios, it is essential to have models that can quantify (1) the probability of a missile impact on a target and (2) the consequences of the probable impact.

Evaluating offshore technologies for produced water management using Greenpro-I - a risk-based life cycle analysis for green and clean process selection and design

Abstract Sustainable development and environmental protection require green products, processes, and waste management strategies. The selection and design of green and clean processes and products involve handling a huge set of data related to the environment, economics and technologies. Therefore, it is essential to employ a comprehensive technique to guide decision-making under uncertainty that can incorporate these factors. The paper presents decision-making under uncertainty based on life cycle analysis and considering environmental, technological, and economical drivers in the analysis. The methodology, GreenPro-1, is applicable at any stage of a process design and can be used to evaluate the environmental burdens of a product or a process throughout its life cycle, and to identify and assess opportunities to make improvements. A weighting scheme is used to integrate various criteria and policies so that a most appropriate alternative can be selected. The GreenPro-1 methodology is applied to a case study for offshore produced water management. In the first stage, 14 best available technologies (BAT) for treatment of produced water are evaluated individually using cradle-to-gate life cycle analysis. In the second stage, seven treatment strategies are developed from BAT and are further investigated. A treatment strategy that combines a hydrocyclone and produced water re-injection proved to be the best system among selected strategies. The combination of hydrocyclone and adsorption was found to be the next best treatment strategy. Two other noteworthy strategies were membrane and centrifuge combination, and down-hole separation and injection.

A virtual experimental technique for data collection for a Bayesian network approach to human reliability analysis

Bayesian network (BN) is a powerful tool for human reliability analysis (HRA) as it can characterize the dependency among different human performance shaping factors (PSFs) and associated actions. It can also quantify the importance of different PSFs that may cause a human error. Data required to fully quantify BN for HRA in offshore emergency situations are not readily available. For many situations, there is little or no appropriate data. This presents significant challenges to assign the prior and conditional probabilities that are required by the BN approach. To handle the data scarcity problem, this paper presents a data collection methodology using a virtual environment for a simplified BN model of offshore emergency evacuation. A two-level, three-factor experiment is used to collect human performance data under different mustering conditions. Collected data are integrated in the BN model and results are compared with a previous study. The work demonstrates that the BN model can assess the human failure likelihood effectively. Besides, the BN model provides the opportunities to incorporate new evidence and handle complex interactions among PSFs and associated actions.

Assessing offshore emergency evacuation behavior in a virtual environment using a Bayesian Network approach

In the performance influencing factor (PIF) hierarchy, person-based influencing factors reside in the top level along with machine-based, team-based, organization-based and situation/stressor-based factors. Though person-based PIFs like morale, motivation, and attitude (MMA) play an important role in shaping performance, it is nearly impossible to assess such PIFs directly. However, it is possible to measure behavioral indicators (e.g. compliance, use of information) that can provide insight regarding the state of the unobservable person-based PIFs. One common approach to measuring these indicators is to carry out a self-reported questionnaire survey. Significant work has been done to make such questionnaires reliable, but the potential validity problem associated with any questionnaire is that the data are subjective and thus may bear a limited relationship to reality. This paper describes the use of a virtual environment to measure behavioral indicators, which in turn can be used as proxies to assess otherwise unobservable PIFs like MMA. A Bayesian Network (BN) model is first developed to define the relationship between person-based PIFs and measurable behavioral indicators. The paper then shows how these indicators can be measured using evidence collected from a virtual environment of an offshore petroleum installation. A study that focused on emergency evacuation scenarios was done with 36 participants. The participants were first assessed using a multiple choice test. They were then assessed based on their observed performance during simulated offshore emergency evacuation conditions. A comparison of the two assessments demonstrates the potential benefits and challenges of using virtual environments to assess behavioral indicators, and thus the person-based PIFs.

An Ecological Risk Assessment Methodology for Screening Discharge Alternatives of Produced Water

ABSTRACT Previous studies on Ecological Risk Assessment (ERA) of produced water relied on the use of deterministic hydrodynamic models. The assessment was usually carried out in the North Sea context using a model such as the Chemical Hazard Assessment and Risk Management (CHARM), or in the North American context based on the output of a hydrodynamic model such as the Cornell Mixing Zone Expert System (CORMIX). In both these cases, however, probabilistic analysis has not been employed, particularly, to account for uncertainty associated with hydrodynamic models in the ERA study. In fact, it is the hydrodynamic model that has a direct linkage to the selection of the discharge alternatives. Apart from the monitoring purposes, in this article, it is suggested that criteria for evaluating discharge alternatives of produced water in a marine environment might incorporate an awareness of ecological risks by incorporating engineering and toxicological aspects. An ERA methodology consisting of problem formulation, analysis, and risk characterization is discussed in light of evaluating the discharge alternatives. A probabilistic analysis using Latin Hypercube Sampling (LHS)–based Monte Carlo (MC) simulations was employed. A depiction of associated risks for an area comparable to a regulatory mixing zone of typical effluent discharges is presented.