Risk Analysis Process Research Articles

Model-assisted process characterization and validation for a continuous two-column protein A capture process.

In this study we introduce three process characterization approaches toward validation of continuous twin-column capture chromatography (CaptureSMB), referred to as "standard," "model assisted," and "hybrid." They are all based on a traditional risk-based approach, using process description, risk analysis, design-of-experiments (DoE), and statistical analysis as essential elements. The first approach, the "standard" approach uses a traditional experimental DoE to explore the design space of the high-ranked process parameters for the continuous process. Due to the larger number of process parameters in the continuous process, the DoE is extensive and includes a larger number of experiments than an equivalent DoE of a single column batch capture process. In the investigated case, many of the operating conditions were practically infeasible, indicating that the design space boundaries had been chosen inappropriately. To reduce experimental burden and at the same time enhance process understanding, an alternative "model assisted" approach was developed in parallel, employing a chromatographic process model to substitute experimental runs by computer simulations. Using the "model assisted" approach only experimental conditions that were feasible in terms of process yield constraints (>90%) were considered for statistical analysis. The "model assisted" approach included an optimization part that identified potential boundaries of the design space automatically. In summary, the "model assisted" approach contributed to increased process understanding compared to the "standard" approach. In this study, a "hybrid" approach was also used containing the general concepts of the "standard" approach but substituting a number of its experiments by computer simulations. The presented approaches contain essential elements of the Food and Drug Administration's process validation guideline.

Risk analysis and assessment in the worksites using the fuzzy-analytical hierarchy process and a quantitative technique – A case study for the Greek construction sector

In this paper, we propose a safety risk assessment process using the Fuzzy Extended Analytic Hierarchy Process (FEAHP) for prioritizing the risks in worksites. Also, the FEAHP which responds the decision maker’s opinion is enhanced by the Proportional Risk Assessment Technique (PRAT), which is a quantitative risk analysis and assessment process applied by using real accidents data. A real-life project example has been used in order to illustrate the functionality and adequability of the process. For validating the effectivity of the proposed framework, we compared the results of the process with the results of treating the example with using only the typical Analytical Hierarchy Process. The proposed framework could be a useful tool for decision makers to estimate the emergency of investing constrained budget in actions preventing specific kind of accidents for achieving the maximum health and safety protection with the minimum cost.

Is spinelessness a stable character in cactus pear cultivars? Implications for invasiveness

Selection against invasiveness allows breeders to continue trading with plant cultivars when the wild species is otherwise invasive. However, to ensure the benefits of a species over the medium-to longer-term, it is necessary to determine whether the selected non-invasive traits are stable characters in the developed cultivars. This is especially true for Opuntia ficus-indica which provides a variety of benefits, especially in the arid and semiarid areas of the world. It does, however, also have a long history of invasion outside its native range, where it causes a wide range of negative impacts. Since the 19th century, spineless cultivars of O. ficus-indica have been developed through plant breeding. Due to their lack of spines, these cultivars can be easily grazed by herbivores and so are generally thought not to cause invasions. Spineless forms are currently widely cultivated worldwide. Nevertheless, if these cultivars were to revert to the spiny form, they could lead to future invasions. We performed germination experiments to explore the likelihood of reversion to spiny forms of nine common spineless O. ficus-indica cultivars. All seedlings grown from spineless cultivars were spiny. Overall, we suggest that further research and a risk analysis process for spineless O. ficus-indica cultivars should be developed.

Quantitative Risk Analysis in Megaprojects Schedules: a critical comparison between the theoretical findings and the empirical evidence from a brazilian oil and gas company

Megaprojects are temporary endeavors that involve high investments and long term results, high degree of uncertainty and risks. The purpose of this article is to stablish a critical comparative analysis of the quantitative risk analysis process in megaproject schedules. The methodology includes a study of the scientific literature and the empirical evidence obtained from interviews with specialists of an oil and gas segment company. A systematic survey of the scientific literature was carried out in ISI Web of Science, Scopus and Scielo databases. The primary data collection was carried out through interviews with a sample of 10 professionals with high level of seniority. As results, the evidences indicate that there is restricted scientific literature on the subject in peer reviewed journals, which suggests an academic contribution of the present study. In addition, Monte Carlo simulation is the most frequent technique used in risk management of megaproject scheduling that provides precision to the probability of project completion data. Besides, it was observed an existence of subjectivities, such as the bias of experts, not judgment of the time estimates of activities, and gaps, such as an inconsistency of the logic of the schedules, no process of quantitative risk analysis. Finally, it was detected the need for training in risk management of hierarchical senior managers, as a way to contribute to the achievement of better results with the use of non-decision-making practice.

Resilience and risk analysis of fault-tolerant process control design in continuous pharmaceutical manufacturing

The shift from batch to continuous manufacturing, which is occurring in the pharmaceutical manufacturing industry has implications on process safety and product quality. It is now understood that fault-tolerant process control of critical process parameters (CPPs) and critical quality attributes (CQAs) is of paramount importance to the realization of safe operations and quality products. In this study, a systematic framework for fault-tolerant process control system design, analysis, and evaluation of pharmaceutical continuous oral solid dosage manufacturing is proposed. The framework encompasses system identification, controller design and analysis (controllability, stability, resilience, etc.), hierarchical three-level control structures (model predictive control, state estimation, data reconciliation, etc.), risk mapping, assessment and planning (Risk MAP) strategies, and control performance evaluation. The key idea of the proposed framework is to identify the potential risks associated with the control system design itself, the material property variations, and other process uncertainties, under which the control strategies must be evaluated. The framework is applied to a continuous direct compaction process, specifically the feeding-blending subsystem, wherein the major source of variance in the process operation and product quality arises. It is demonstrated, using simulations and experimentally, that the process operation failures and product quality variations in the feeding-blending system can be mitigated and managed through the proposed systematic fault-tolerant process control system design and risk analysis framework.

Risk analysis of sequential processes in food industry integrating multi-stage fuzzy cognitive map and process failure mode and effects analysis

Abstract Since managers and staff have not understood the actual consequences of risks in the food industry well, risk management methods practically are limited to identification of the type of risks. In addition, created changes in the business environment have led to change in the attitude of risk management to a process-oriented and systematic view. Because managers cannot decide based on the output of risk management process to implement improvement projects and allocate resources to them. This study has been tried to exactly identify and prioritize potential failures of the production process by using an approach based on the multi-stage Fuzzy Cognitive Map (FCM) method and Process Failure Mode and Effects Analysis (PFMEA) technique with the help of the cross-functional team. In this approach, failures are prioritized according to the amount of impact of each failure on other failures, as well as the amount of three factors as severity, occurrence, and detection (outputs of PFMEA). This approach considers process-oriented view in manufacturing system through internal-stage and external-stage relationships between production process failures and covers disadvantages of traditional Risk Priority Number (RPN) score such as disregarding internal relationships between failures. Hence, prioritization of potential failures based on the score which includes RPN determinant factors and causal relationships between failures is performed using the multi-stage FCM and learning algorithm based on extended Delta rule. The results of the proposed approach’s implementation in an active company in the food industry show that prioritization of failures is closer to reality and presents more full prioritization in comparison with approaches such as traditional RPN. The real case study in the food industry has been used to show the ability of the proposed approach.

Perspectives on the nexus between good risk communication and high scientific risk analysis quality

In general, successful risk communication can be said to require “an understanding of the target audience, including the best means for reaching the audience: a credible or trusted source; and a message that has ideally been pre-tested to ensure its effectiveness” [43]. The scientific quality of the risk analysis is not questioned. The sources can be credible or trusted, but the scientific risk analysis quality can be poor. For example, the risk communication can be based on a scientifically unsound risk characterisation, yet be communicated successfully if the reference is a communication perspective as above. Good risk communication cannot, however, be seen in isolation from the broader process of risk analysis and management. The present paper provides some reflections on this topic, the main aim being to strengthen the argumentation for the thesis that scientific and foundational issues of risk analysis are critical for the successful communication of risk. Several examples are used to demonstrate this thesis.

A quantitative risk assessment methodology for construction project

It is observed that most of the infrastructure projects fail to meet their cost and time constraints, which will lead to a low return on investment. The paper highlights that the present risk management tools and techniques do not provide an adequate basis for response selection in managing critical risks specific to infrastructure projects. This paper proposes a risk quantification methodology and demonstrates its application for an industrial construction project. A case study is used to present an application of the proposed risk management methodology to help organisations efficiently choose risk response strategy and allocate limited resources. The research adopts an integrated approach to prioritize risks using Group Technique for Order Preference by Similarity to Ideal Solution (GTOPSIS) and to quantify risks in terms of overall project delays using Judgemental Risk Analysis Process (JRAP), and Monte Carlo Simulation (MCS). A comparison between the results of qualitative risk analysis using GTOPSIS and quantitative risk analysis i.e., JRAP and MCS is presented. It is found that JRAP along with MCS could provide some powerful results which could help the management control project risks. The crux of this paper is that the risks are highly dependent on project schedule and the proposed methodology could give a better risk priority list because it considers slackness associated with the project activities. The analysis can help improve the understanding of implications of specific risk factors on project completion time and cost, while it attempts to quantify risks. In turn, this enables the project manager to devise a suitable strategy for risk response and mitigation.

Risk Assessment of Bulgarian Electricity Network under Natural Disasters

The electricity transmission and distribution networks are heavily affected from natural disasters. Electricity networks can also incur multiple or multi-risks, including their conjoint and cascading effects, or systemic risks. The aim this paper is to propose an approach for risk assessment of Bulgarian electricity transmission and distribution network under natural disasters. The proposed approach for risk assessment is based on guidelines and techniques of risk management standards ISO 31000:2009 and EN 31010:2010. Here, the risk assessment is the overall process of risk identification, risk analysis and risk evaluation. The risk assessment results can support the all key stakeholders to take more informed decision about effective protection of the electricity networks from natural disasters.

Фінансовий ризик-менеджмент з урахуванням інформаційних ризиків

The existing risk management methods, such as risk aversion, localization, diversification and risk compensation are investigated and the whole process of risk analysis based on the system methodology through the main stages: detection, analysis and minimization of risk is illustrated. The criterion for ta-king into account information risks through possible losses in the implementation of information threat is proposed. The main criteria for analyzing information risks through the assessment of the quality of data, the decisions taken and the adequacy of the modeling are given, and the own quadratic criterion is proposed to take into account the compensatory costs for combating the risks of both information and financial nature. Also, the system for financial risk management, which takes into account both informational and financial risks and forms a managerial decision is shown. The process of work of the risk management system is adaptive, that is, on the basis of the proposed criteria for assessing the quality of the decisions made, it is possible to accept, reject or re-develop a management solution based on the set of input data and modeling results. The proposed methodology for analyzing and evaluating information risks within financial risks has been implemented in a unified information technology on the SAS platform.

Integrated Safety and Process Optimization Approach for Ammonia Synthesis Loop

Among the process safety strategies, inherent safety, proposed by Kletz, has been widely accepted and applied for the preliminary design of chemical processes. Process optimization with safety aspect has usually focused on accident costs rather than risk. Additionally, the significance of inherent assessment for process optimization has been overlooked. Simultaneous optimization of risk with inherent safety and process economics can be more impactful in practical process designs. Therefore, a new methodology is consolidated in this paper, such that an external risk and process economics model is coupled with HYSYS to achieve an optimized process design. Here, process stream index (PSI) has been suggested to identify the critical process streams. For identified critical process streams, risk analysis and process economics are studied. Risk estimation and process economics parameters are studied in an external model by extracting information from HYSYS through VBA coding. For unacceptable risk, minimization guide word is used to improve the performance of critical streams. With inherent safety implementation, process economics is also found to be improved, as 3.2 less energy would be required without any significant change in ammonia product quantity. This approach would serve to deal safety with process attributes as multi-objective optimization method of process design. © Universiti Malaysia Pahang, Malaysia.

Risk Analysis and Evaluation Process of PDM Project Based on Fuzzy Analytic Hierarchy

Based on the comprehensive analysis of the major risk factors of PDM project, this paper establishes risk evaluation index system. In addition, it determines the weight of various indexes by adopting the thought of quantifying the expert’s opinions and applying the fuzzy analytic hierarchy process and evaluates the enterprise risk in PDM project by using fuzzy synthetic evaluation method. It provides bases for the enterprise to make decisions about PDM project and take risk control measures.

Evolutionary biology and the risk of scrapie disease in sheep.

Scrapie in sheep occurs in two forms. Classical scrapie, along with chronic wasting disease in deer, differs from all other prion diseases in being contagious and occurring as propagating epidemics. By contrast, atypical scrapie occurs sporadically and like most other prion diseases, such as Creutzfeldt-Jakob disease in humans, is regarded as non-contagious under natural conditions. This difference motivates the present study, which uses the perspective of evolutionary biology to explore whether atypical or non-contagious scrapie can transform to classical or contagious scrapie when appropriate evolutionary forces are in play. The study begins with an explanation of relevant evolutionary concepts such as evolvability and object of selection and proposes some explicit definitions of key terms such as variation, replication, heritability and strain. Observations of the behaviour of scrapie strains during serial passage in sheep, goats and mice assign the functions of variation, replication and heritability to the scrapie agent and establish it as an object of selection with a capacity for evolvability. Similar observations of strain behaviour in other prion diseases establish prions in general as objects of selection that are able to evolve. Accordingly, it is tenable that non-contagious atypical scrapie can provide progenitors for the prions causing contagious classical scrapie and can be a latent epidemiological threat. A comparison of atypical and classical scrapie identifies an affinity of the scrapie agent for lymphoreticular tissue and predilections for portals of exit and entry as key gain-of-function mutations that endow prion populations in atypical scrapie with the function of communicability or contagiousness. The present study can be seen as the hazard identification step within a process of risk analysis that systematises knowledge in aid of animal health and welfare and biosecurity. It justifies completion of the remaining steps in risk assessment. This task can be assisted by further comparisons of atypical and classical scrapie enlightened by historical records of scrapie disease, the epidemiology of atypical scrapie, the general behaviour of prions and evolutionary biology.

Risk monitoring through better knowledge-based risk processes

Knowledge-based risk processes are becoming a key factor in the effective monitoring of risk. Therefore, knowledge-based risk processes and knowledge-based risk repositories are extensively used in information technology (IT) projects to successfully support risk monitoring. The aim of this paper is to propose a model that describes the integration of knowledge-based risks (via the processes of knowledge-based risk identification, analysis, evaluation and education) and knowledge-based risk repositories to support risk monitoring. This research paper utilizes the quantitative research method through questionnaires and the combined responses of 135 people employed in ten IT companies across Jordan. We applied smart partial least squares (PLS) software to analyze all our hypotheses. Our findings suggest that there is a constructive effect to acclimating knowledge-based risk identification, knowledge-based risk analysis and knowledge-based risk education when improving the risk monitoring mediation of a given knowledge-based risk repository.

МЕТОД ОЦЕНКИ ДОСТОВЕРНОСТИ КОЛИЧЕСТВЕННОГО АНАЛИЗА РИСКА НА ОБЪЕКТАХ НЕФТЕГАЗОВОЙ ОТРАСЛИ

Despite the existing arsenal of various methods of quantitative risk analysis both in Russian and international practice, problems arise related to the assessment of the reliability of the results obtained during their practical application, including at oil and gas facilities. The question remains to what extent the decision-maker can trust the results. The article considers the problem of assessing the reliability of quantitative risk analysis at oil and gas facilities. Existing methods for estimating reliability are investigated. It is proposed to use an approach based on ensuring the quality of the process of risk analysis itself. To increase the objectivity in assessing the reliability of the results of a risk analysis, a formal quantitative method was proposed. The article introduces 5 criteria that ensure the quality of the process of risk analysis at oil and gas facilities. A system of rules for coding the values of each of the basic criteria into three discrete qualitative levels was developed. The solution of the task was accomplished by constructing a classifier in which the reliability index of a quantitative risk analysis of oil and gas industry objects is a function of the values of the basic criteria. The reliability of the risk analysis was evaluated on the basis of a naive Bayesian classifier that takes into account the values of the five basic criteria in the evaluation framework. The results of the classifier work are based on a variety of training data that were previously evaluated by experts. The article suggests an approach to the assessment of the quality of the classifier itself, based on a cross-checking with successive exclusion of one copy of the training data. The merits of using the naive Bayesian classifier for assessing the reliability of quantitative risk analysis in oil and gas industry objects include the fact that the classification is carried out quite easily and quickly, surpasses many other algorithms, and requires a smaller amount of training data. A naive Bayesian classifier works very well with categorical features, which is exactly what is reflected in this article.

Fuel distribution process risk analysis in East Borneo

Fuel distribution is an important aspect of fulfilling the customer’s need. It is risky because it can cause tardiness that can cause fuel scarcity. In the process of distribution, many risks are occurring. House of Risk is a method used for mitigating the risk. It identifies seven risk events and nine risk agents. Matrix occurrence and severity are used for eliminating the minor impact risk. House of Risk 1 is used for determining the Aggregate Risk Potential (ARP). Pareto diagram is applied to prioritize risk that must be mitigated by preventive actions based on ARP. It identifies 4 priority risks, namely A8 (Car trouble), A4 (Human Error), A3 (Error deposit via bank and underpayment), and A6 (traffic accident) which should be mitigated. House of Risk 2 makes for mapping between the preventive action and risk agent. It gets the Effectiveness to Difficulty Ratio (ETD) for mitigating action. Conducting safety talk routine once every three days with ETD 2088 is the primary preventive actions.

A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry

The high hazard mechanical system (HHMS) has three characteristics in the petroleum and petrochemical industry (PPI): high risk, high cost, and high technology requirements. For a HHMS, part, component, and subsystem failures will result in varying degrees and various types of risk consequences, including unexpected downtime, production losses, economic costs, safety accidents, and environmental pollution. Thus, obtaining the quantitative risk level and distribution in a HHMS to control major risk accidents and ensure safe production is of vital importance. However, the structure of the HHMS is more complex than some other systems, making the quantitative risk analysis process more difficult. Additionally, a variety of uncertain risk data hinder the realization of quantitative risk analysis. A few quantitative risk analysis techniques and studies for HHMS exist, especially in the PPI. Therefore, a study on the quantitative risk analysis method for HHMS was completed to obtain the risk level and distribution of high-risk objects. Firstly, Fuzzy Set Theory (FST) was applied to address the uncertain risk data for the occurrence probability (OP) and consequence severity (CS) in the risk analysis process. Secondly, a fuzzy fault tree analysis (FFTA) and a fuzzy event tree analysis (FETA) were used to achieve quantitative risk analysis and calculation. Thirdly, a fuzzy bow-tie model (FBTM) was established to obtain a quantitative risk assessment result according to the analysis results of the FFTA and FETA. Finally, the feasibility and practicability of the method were verified with a case study on the quantitative risk analysis of one reciprocating pump system (RPS). The quantitative risk analysis method for HHMS can provide more accurate and scientific data support for the development of Asset Integrity Management (AIM) systems in the PPI.

Risk based control of the negative effect of discontinued automated processes – a case from the agricultural domain

The current paper delineates a modern algorithmic procedure for estimating the risk and calculating a realistic duration of interrupted critical computerized business activities, in order to mitigate or prevent their corresponding negative consequences. The contribution is formulated via merging risk management and business continuity concepts. The formulation of an integrated business continuity management policy includes the proactive determination of approximate recovery timeframes for critical business functions. Practically, this estimation is based on recovery tests which are executed under ideal conditions, and unexpected factors which may emerge during a real process interruption and signifi cantly delay its recovery are ignored. Agriculture is a domain where the incorporation of an integrated business continuity management system is a crucial issue. The interruption of agricultural computerized activities can be triggered by and can result to various undesirable environmental phenomena. Thus, especially for agriculture, the consideration of unexpected factors when executing recovery tests is highly demanded. The currently presented algorithm accepts as initial input the estimated recovery time which is based on recovery exercises executed under ideal conditions. Then, a precise number of potential unpredictable hazards (factors) are taken into consideration and the risk magnitude of each threat is semi-quantitatively estimated. The total risk magnitude is utilized to estimate the time deviation from the initially defi ned recovery time. After the risk analysis process is terminated, a new recovery timeframe is proposed. The time deviation from the initially defi ned recovery time is calculated in its absolute value. The algorithm is fi nally validated by applying the calculated extended timeframe to the system availability formula which measures the achieved system availability levels for any information system. The validation of the approach is demonstrated via a practical case study from the agricultural domain, namely the greenhouse irrigation scheduling system interruption scenario.

Development of a Premium Quality Plasma-derived IVIg (IQYMUNE®) Utilizing the Principles of Quality by Design-A Worked-through Case Study.

Polyvalent human normal immunoglobulins for intravenous use (IVIg), indicated for rare and often severe diseases, are complex plasma-derived protein preparations. A quality by design approach has been used to develop the Laboratoire Français du Fractionnement et des Biotechnologies new-generation IVIg, targeting a high level of purity to generate an enhanced safety profile while maintaining a high level of efficacy. A modular approach of quality by design was implemented consisting of five consecutive steps to cover all the stages from the product design to the final product control strategy.A well-defined target product profile was translated into 27 product quality attributes that formed the basis of the process design. In parallel, a product risk analysis was conducted and identified 19 critical quality attributes among the product quality attributes. Process risk analysis was carried out to establish the links between process parameters and critical quality attributes. Twelve critical steps were identified, and for each of these steps a risk mitigation plan was established.Among the different process risk mitigation exercises, five process robustness studies were conducted at qualified small scale with a design of experiment approach. For each process step, critical process parameters were identified and, for each critical process parameter, proven acceptable ranges were established. The quality risk management and risk mitigation outputs, including verification of proven acceptable ranges, were used to design the process verification exercise at industrial scale.Finally, the control strategy was established using a mix, or hybrid, of the traditional approach plus elements of the quality by design enhanced approach, as illustrated, to more robustly assign material and process controls and in order to securely meet product specifications.The advantages of this quality by design approach were improved process knowledge for industrial design and process validation and a clear justification of the process and product specifications as a basis for control strategy and future comparability exercises.

Fuzzy based risk prioritisation in an auto LPG dispensing station

A fuzzy rule based inference model assessing the failure modes for risk ranking in FMEA to manage risks and make maintenance decisions is applied to a LPG refuelling station in this paper. Normally in FMEA, risk priority number (RPN) is determined by multiplication of feature scores that are inferred from the degree or probability of occurrence, severity and non detection of the problem, without taking into consideration the relative importance of factors. In fuzzy approach, linguistic assessment of factors is evaluated to obtain risk priority number. A rule based fuzzy inference engine generates the priority ranks of identified failure modes. Direct evaluation is possible with the aid of grey theory by assigning weights to the features in the absence of expertise to develop an inference rule base. The GRA approaches can solve the problem of risk prioritising, which needs expertise to develop rule base while building the fuzzy inference system. By applying fuzzy FMEA and fuzzy logic with grey relational approach (GRA), expert linguistic opinions are used to rank the identified failure modes and the results are presented. The risk of the failure modes are ranked in an inclusive approach based on the fuzzy domain projections. It is effective and feasible to handle various types of uncertainties, such as incompleteness, fuzziness, imprecision, and so on, in the risk analysis process.