Modelling, simulation and analyses of systems with breakdown imposed scrapping

Integrated asset modelling allows coupling fluid behavior in the full production system, from the reservoir to the facilities. This approach is crucial when different field development scenarios need to be evaluated during concept selection. Different situations must be systematically examined allowing for the optimal operational constraints (rate and pressure limits) and the dimensions of individual elements to be defined, whilst ensuring that material balance considerations are honoured. Traditionally, reservoir, production and facilities groups separately conduct field modelling and simulations for each element in the system, as part of an iterative process. Reservoir engineers receive facilities constraints and lift performance curves from production engineers as an input. In return, production and facilities engineers receive production profiles from reservoir engineers and design the appropriate facilities capable of safely processing and transporting the produced fluids throughout the lifetime of the project. Any changes in production configuration affect all the models and leads to a need for each discipline to revaluate their simulation models. This process often results in approximations being adopted and frequently raises challenges in the audit trail, as assumptions may be inadequately documented. Integrated simulation generates representative field production forecasts and the ability to quantify cumulative production and key performance indicators for each scenario. As an example, rates, erosional velocities, pressures and temperatures profiles, compressor and pump requirements and many other variables may be explicitly studied throughout the entire life of the field. This approach reduces the risk of facilities bottlenecking and flow assurance becoming an HSE issue. This paper describes some of the integrated asset management studies conducted to evaluate different strategies for the development of an ultra-deep water exploratory block, and the key technical factors considered. For each concept studied, multi-disciplinary models were created to simulate reservoir, tubing, flowline and surface network behaviour for gas condensate, light oil and water production streams. Subsea separation and surface process facilities options were modelled to coordinate the work streams of the different engineering domains, in a coupled solution. Some complex development scenarios were included. A gas-reinjection case was modelled, where the separated gas from process simulation model is reinjected into the reservoir. Another scenario considered an ultra-deep subsea separation and boosting unit, which was modelled by process simulation software, connected to both the surface network and the reservoir simulation. All were linked with the FPSO process simulation. A range between 5 to 17% of oil gain was obtained in the different scenarios. As a result of these studies, the decision making process for future investments in the field development plan was optimized.

Software process simulation has been evolved towards a mature technology for analyzing the behavior of software processes: Methods for systematically developing simulators exist, tools for modeling and execution are available, models for nearly all kinds of processes have been created, and empirical evidence on the accuracy of many models has been gathered. However, software process simulation is still waiting for a breakthrough success. Although simulation is a technology that has been successfully established in many domains, software process simulation is not widely used in software engineering practice. Should we pivot or persevere? This article argues that it is necessary to use a rigorous approach for discovering customers of process simulators and finding out what they consider to be value. One mechanism to do this is to apply so-called validated learning, i.e., to apply an actionable learning approach to identify what is relevant and what is irrelevant by systematically testing value hypotheses. Doing this promises that simulation efforts can be concentrated on value-creation and that wrong avenues can be avoided. Besides this, the article sketched prerequisites and lessons learned that need to be considered when applying simulators in practice as well as upcoming opportunities for making software process simulation a success.

BackgroundDuring the COVID-19 pandemic, medical laypersons with symptoms indicative of a COVID-19 infection commonly sought guidance on whether and where to find medical care. Numerous web-based decision support tools (DSTs) have been developed, both by public and commercial stakeholders, to assist their decision making. Though most of the DSTs’ underlying algorithms are similar and simple decision trees, their mode of presentation differs: some DSTs present a static flowchart, while others are designed as a conversational agent, guiding the user through the decision tree’s nodes step-by-step in an interactive manner.ObjectiveThis study aims to investigate whether interactive DSTs provide greater decision support than noninteractive (ie, static) flowcharts.MethodsWe developed mock interfaces for 2 DSTs (1 static, 1 interactive), mimicking patient-facing, freely available DSTs for COVID-19-related self-assessment. Their underlying algorithm was identical and based on the Centers for Disease Control and Prevention’s guidelines. We recruited adult US residents online in November 2020. Participants appraised the appropriate social and care-seeking behavior for 7 fictitious descriptions of patients (case vignettes). Participants in the experimental groups received either the static or the interactive mock DST as support, while the control group appraised the case vignettes unsupported. We determined participants’ accuracy, decision certainty (after deciding), and mental effort to measure the quality of decision support. Participants’ ratings of the DSTs’ usefulness, ease of use, trust, and future intention to use the tools served as measures to analyze differences in participants’ perception of the tools. We used ANOVAs and t tests to assess statistical significance.ResultsOur survey yielded 196 responses. The mean number of correct assessments was higher in the intervention groups (interactive DST group: mean 11.71, SD 2.37; static DST group: mean 11.45, SD 2.48) than in the control group (mean 10.17, SD 2.00). Decisional certainty was significantly higher in the experimental groups (interactive DST group: mean 80.7%, SD 14.1%; static DST group: mean 80.5%, SD 15.8%) compared to the control group (mean 65.8%, SD 20.8%). The differences in these measures proved statistically significant in t tests comparing each intervention group with the control group (P<.001 for all 4 t tests). ANOVA detected no significant differences regarding mental effort between the 3 study groups. Differences between the 2 intervention groups were of small effect sizes and nonsignificant for all 3 measures of the quality of decision support and most measures of participants’ perception of the DSTs.ConclusionsWhen the decision space is limited, as is the case in common COVID-19 self-assessment DSTs, static flowcharts might prove as beneficial in enhancing decision quality as interactive tools. Given that static flowcharts reveal the underlying decision algorithm more transparently and require less effort to develop, they might prove more efficient in providing guidance to the public. Further research should validate our findings on different use cases, elaborate on the trade-off between transparency and convenience in DSTs, and investigate whether subgroups of users benefit more with 1 type of user interface than the other.Trial RegistrationDeutsches Register Klinischer Studien DRKS00028136; https://tinyurl.com/4bcfausx (retrospectively registered)

Clinical Decision Support Tools

Depending on the complexity of organizational processes, the development of process simulation models might be a very demanding activity in which simulation professionals describe business processes using a specific simulation language. Besides being a difficult and error prone activity, process simulation modelling is also a major reason why simulation projects have a tendency to take too much time to be accomplished. In an effort to contribute to improve the situation, in this paper we present an approach in which previously developed process simulation components are used in the context of simulation projects. Using this approach, new process simulation models are developed in a component-based fashion by simply selecting pieces of simulation functionality from a repository of process simulation components, which were previously developed and properly tested. As an example, by taking advantage of the componentization mechanisms available in a specific simulation tool, we have developed a small set of process simulation components which were stored in a repository and then used in the development of a new business process simulation model.

We review the state of the art of process modeling for discrete event simulation, make a number of observations and identify a number of issues that have to be tackled for promoting the use of process modeling in simulation. Process models are of particular interest in model-based simulation engineering approaches where the executable simulation model (code) is obtained with the help of textual or visual models. We present an illustrative example of model-based simulation development.

Hybrid modelling for remote process monitoring and optimisation

Background and Purpose: Business process simulation (BPS) model is based on real-life data form sources like databases, observations and interviews. It acts as “as-is” business scenario can used for reengineering. The main challenge is to gather relevant data and to develop simulation model. Research aims to elaborate BPS model and to systematically assess how close to reality it is. Design/Methodology/Approach: The research has been performed in Polish telecommunications company. Authors investigate technical process of expanding cellular network. After elaborating “as-is” model, authors use ADONIS simulation tool to run a series simulations and confront simulation results with actual historical events. After this, assessment whether computer simulation model can precisely map real-life business process - and consequently act as credible basis for process improvement - is made. Results: The simulation model has been constructed with data from the WfMS database, observations, staff knowledge and their experience. Fully equipped simulation model is found to allow reconstructing the historical execution of business activity with low margin for error. Some limitation were identified and discussed. Conclusion: BPS is not a popular approach for process reengineering and improvement yet. Data collection issues for BPS that require adopting process mining techniques and additional information sources are among the reasons for that. In our study, computer simulation outputs are compatible with historical events. Hence the model reflects the business reality and can be taken as a reference model while redesigning the process.

Gentle remediation options (GRO) are risk management strategies/technologies that result in a net gain (or at least no gross reduction) in soil function as well as risk management. They encompass a number of technologies, including the use of plant (phyto‐), fungi (myco‐), and/or bacteria‐based methods, with or without chemical soil additives or amendments, for reducing contaminant transfer to local receptors by in situ stabilization, or extraction, transformation, or degradation of contaminants. Despite offering strong benefits in terms of risk management, deployment costs, and sustainability for a range of site problems, the application of GRO as practical on‐site remedial solutions is still in its relative infancy, particularly for metal(loid)‐contaminated sites. A key barrier to wider adoption of GRO relates to general uncertainties and lack of stakeholder confidence in (and indeed knowledge of) the feasibility or reliability of GRO as practical risk management solutions. The GREENLAND project has therefore developed a simple and transparent decision support framework for promoting the appropriate use of gentle remediation options and encouraging participation of stakeholders, supplemented by a set of specific design aids for use when GRO appear to be a viable option. The framework is presented as a three phased model or Decision Support Tool (DST), in the form of a Microsoft Excel‐based workbook, designed to inform decision‐making and options appraisal during the selection of remedial approaches for contaminated sites. The DST acts as a simple decision support and stakeholder engagement tool for the application of GRO, providing a context for GRO application (particularly where soft end‐use of remediated land is envisaged), quick reference tables (including an economic cost calculator), and supporting information and technical guidance drawing on practical examples of effective GRO application at trace metal(loid) contaminated sites across Europe. This article introduces the decision support framework. ©2015 Wiley Periodicals, Inc.

Process modeling is widely used in the design of both integrated circuits themselves and in the design of integrated circuit manufacturing processes. Director1 gives an overview of the roles of process, device, and circuit simulators in the overall design process. Process simulation has been used for many years for more mature processes such as oxidation, diffusion, and ion implantation, and the widely used computer simulation programs for these processes use physically based, first principles models.2,3 Simulation is also widely used to predict the performance of resist development, etching, and film deposition processes.4,5 However, the process simulation models which are currently used to predict the performance of these processes are not first principles models. The current generation of plasma etching and deposition models is based on heuristic rules which describe the propagation of a moving boundary rather than on the solution of the fundamental equations which describe the underlying, physical processes. In fact, the required input data for current plasma etching and deposition models are experimentally measured etch and/or deposition rate values.

Simulation software Tecnomatix Plant Simulation was originally created for a modelling and subsequent simulation of production and logistics processes. Its variability, however, opens its use also in other areas such as transport in urban agglomerations. Based on that, research was implemented to verify the program’s application in urban transport, specifically to visualize and simulate traffic processes at the traffic node. The paper describes a methodology which made it possible to create a simulation program for traffic light intersections, and presents examples of the simulation model application. The proposed methodology will enable the application of Tecnomatix Plant Simulation to create a complex simulation model of the logistics process. It will mainly enable to simulate the field of production logistics and city logistics within one simulation model.

The influence of cognitive science and psychology on decision theory is bringing about changes to assumptions about decision making, and, as a consequence, the way that decisions should be modeled and supported. The three articles published in this Special Issue on intelligent decision support and modeling reflect an emerging literature that incorporates psychology into decision modeling and decision support. This literature represents only a starting point, and much remains to be done in terms of acknowledging the influence of psychology on engineering decisions. Decision support tools will probably never completely make up for engineers’ lack of the cognitive capacity needed to make the multitude of decisions associated with engineering design in a fully informed, unbiased way. Incorporating rational choice theory and psychology into decision support tools seems to be a fruitful path toward promoting optimal decisions in engineering. The last two to three decades have brought about important changes to the field of decision theory, particularly in response to the generally accepted principle that the act of cognitive representation and framing of decisions should follow the axioms of expected utility theory. The standard theory for decision making is based on subjective expected utility theory (e.g., Savage, 1954; Schmeidler, 1989), which involves the enumeration of possibilities, an analysis of the possible outcomes, and the selection of the utility-maximizing decision (Gilboa & Schmeidler, 2001). In engineering design, decision theory is generally applied as a systematic procedure for selecting design variables when there is uncertainty over the preferences associated with the objectives (Thurston, 1991, 2001). In schools of engineering, operations research, computer science, and business around the world, students continue to be taught a set of methodologies consonant with rational choice theory (Wood, 2004), which is founded upon the analysis of information as the basis of decision. In short, decision support and modeling in engineering design generally follows a framework of selecting design variables that optimize the expected utility of the design, and, in so doing, casts engineering design within a rational process (Hazelrigg, 1998). The perceived increased accuracy and rigor of the decisions and the decision support systems built upon the premise of utility maximization can mask the realities of eliciting preferences from engineers, which may be subject to psychological biases. Kahneman and Tversky (Tversky & Kahneman, 1974; Kahneman & Tversky, 1979) drew attention to the realities of human decision making in describing the heuristics that human beings employ in decision making under uncertainty, which are subject to psychological biases that can lead to systematic and predictable errors. In recent years, Kahneman and colleagues have published a series of articles dealing with strategies to correct for these psychological biases (e.g., Kahneman & Lovallo, 1993; Kahneman et al., 2011) and entire fields of behavioral finance, behavioral economics, and behavioral strategy have grown up around the application of cognitive science and psychology to the theory and practice of decision making under uncertainty in specific contexts. This influence is now being felt in the engineering design domain in the modeling and support of engineering decisions, which is built upon a rich heritage of studies on the cognitive and behavioral strategies of engineers. The three articles published in this Special Issue reflect a shift away from normative subjective utility maximization as the only model for decision making and decision support toward greater consideration to the exigencies of engineering decision making in practice. All three articles contribute to decision support and modeling and treat these as integrated issues rather than as compartmentalized problems. The article “Bayesian Project Diagnosis for the Construction Design Process” by Matthews and Philip is perhaps the most “traditional” of the three articles. The article deals with the problem of forecasting potential problems in construction processes. We use traditional in the sense that Bayesian modeling is an accepted method for estimating the probability of an event or outcome of interest (Marshall & Oliver, 1995). They model the construction process as a Markov chain, with transition probabilities associated with progressing through various Reprint requests to: Andy Dong, Faculty of Engineering and Information Technologies, University of Sydney, Engineering Building (J05), Sydney 2006, Australia. E-mail: andy.dong@sydney.edu.au Artificial Intelligence for Engineering Design, Analysis and Manufacturing (2012), 26, 371–373. # Cambridge University Press 2012 0890-0604/12 $25.00 doi:10.1017/S0890060412000248

Fused deposition modeling (FDM) offers many advantages over conventional manufacturing methods, but it is limited by the number of materials available. Extending FDM technology to semicrystalline polymers has been challenging due to the crystallization that occurs during cooling which results in FDM part warpage. Previous work used process simulation models to study the effects of material parameters and FDM process variables on the part warpage seen using polypropylene (PP). In this work, the process simulation models were adapted to investigate warpage of FDM parts made with a high-performance semicrystalline polymer, polyphenylene sulfide (PPS). Material parameters in the PPS process simulation models were individually changed to the PP values to investigate which material parameters cause PP to exhibit higher warpage than PPS. Material parameters of interest included coefficient of thermal expansion (CTE), thermal conductivity, heat capacity, and Young’s modulus. Additional material parameters based on material property modification through the addition of fillers were investigated in order to establish the relationship between material parameters and warpage values. The simulation models suggested that the CTE has the largest impact on FDM part warpage. Decreasing the CTE in the simulation model resulted in a decrease in the FDM part warpage by the same factor.

Over the last few decades, research on the abatement of carbon dioxide (CO2) gas has gained momentum, due to its increasing atmospheric levels. This study investigated high‐temperature steam‐only gasification of woody biomass for the production of high‐purity hydrogen integrated with CO2 capture in a moving‐bed gasifier. Extensive process modelling and simulation were performed using the superior solid handling features of the Aspen Plus process simulator software. After validating the model with experimental data from a demonstration plant available in the open literature, a reversible carbonation‐calcination reaction of calcium oxide (CaO) with CO2 was added to the system. Sensitivity analyses were conducted to verify the predictive accuracy of the model. The effects of steam‐to‐carbon (S/C) ratio on the resulting gas composition were thoroughly studied to delineate the complex process of gasification. Beyond the mitigation of CO2 emissions, the introduction of a CaO‐based sorbent in the process simulation significantly enhanced hydrogen production by simultaneously promoting the forward water‐gas shift reaction and reducing tars through increased tar‐cracking reactions. The results show that hydrogen of a higher purity was produced with the inclusion of dry‐sorption CO2 capture in the gasification process. Moreover, the addition of the sorbent increased the higher heating values (HHV) by 3 times and improved the cold gas efficiency by 34 %.

Analysis of many critical systems is usually based on the simulation of numerical models. This solution is suitable for analyzing systems with continuous and deterministic behaviors that evolve over time. However, real critical systems are more complex and can exhibit non-deterministic behavior due to unexpected events. Furthermore, critical systems present both discrete and continuous behaviors, which interact regularly. Both features can be modeled with hybrid formal methods, taking advantage of exploration techniques like model checking. We have selected dam management as a case study. A dam is a critical system that has a hybrid behavior, there are continuous variables such as the water level, and discrete states such as the opening degrees of the spillways. At present, Decision Support Systems, based on numerical models, are used to manage complete river basins. Dams are modelled as black boxes which store and release water. A Decision Support Tool (DST) for dam management provides information about the possible consequences of dam operator actions, which can help to ensure the safety of the dam, as well as the efficient use of the water. In this work we have used formal methods to model a dam as a hybrid system, and we have obtained decision support information from the analysis performed with model checking.