Abstract

     A tolerance framework is developed to address a group multiple criteria ranking problem with indirect preference information, which is referred to as the interaction, importance and tolerance of criteria as well as pairwise comparisons among alternatives and criteria. Choquet integral preference model is employed to capture the interaction, importance and tolerance of criteria, all of which are specific to decision makers (DMs). Some mandatory/sufficient requirements concerning criteria which are global or local, also called the tolerance attitudes of DMs, are quantified as tolerability constraints. Preference disaggregation analysis is extended to solve this type of tolerability constraints for preference elicitation. Confronted with the inconsistency issue (the feasibility of the whole preference constraints translated from indirect preference information), cause oriented strategy and consequence oriented strategy are established by regression-based mixed 0–1 integer linear programs with the objectives of the most credible minimal inconsistent preference constraints and the most credible maximal preference constraints in the context of group decision making. Considering a wide range of the minimal unsatisfied subsets of preference constraints responsible for the inconsistency and the maximum satisfied subsets of preference constraints as a consistent result, to reach a robust decision, stochastic multicriteria acceptability analysis (SMAA)-like simulation analysis is generated to examine the whole instances of compatible preference models in the modified feasible preference polyhedron and compute the result in a probabilistic form. Simulation experiment is conducted to investigate the influence of global tolerance attitudes of DMs on preference elicitation in conservative and radical scenarios. Finally, the application of the proposed approach to a credit ranking of small and medium-sized enterprises (SMEs) and the comparison analysis with objective methods are presented and discussed for the effectiveness of the proposed tolerance framework.

Introduction

     Many real-world decision problems in different fields can be formulated as multiple criteria decision making (MCDM) as the performance of alternatives can be decomposed as the evaluation of alternatives on considered criteria and ranked by the comprehensive score which is a straightforward intuition for decision makers (DMs) to comprehend. The preference model in MCDM would be paid more attention to the property that is consistent with observable properties of human decision process (i.e., intuitive reasoning, common sense, and expert knowledge), otherwise it would deteriorate a justifiable decision. The theory of fuzzy measures and integrals Liginlal and Ow (2006) has emerged to characterize the preference of DMs with the realistic hypothesis about preferential dependence among criteria and give the opportunity to represent and interpret the typical human decision process, which is such a predominant character that can address numerous practical preference modelling.

     The DMs have distinguished aggregation behaviors in context of group MCDM, also called the tolerance attitudes of DMs in some literatures. From a mathematical perspective, the tolerance attitudes of DMs are equivalent to the tolerance (or intolerance) of criteria represented by the aggregation operator (Li, Yao, Sun, & Wu, 2018). In a strategic investment decision problem, when searching for most qualified candidates among prospective alternatives, every candidate failing at most k criteria would be rejected. It implies k-intolerance attitudes of the DMs which can be perceived as a selection policy. In the field of fuzzy aggregation operator field, ordered weighted averaging (OWA) operator was studied which used the global tolerance of criteria to measure the optimism degree of DMs (Kim and Ahn, 2018, Zeng et al., 2018). Based on the k-tolerance (or k-intolerance) properties of the Bonferroni mean aggregation operator (Beliakov & James, 2013), Dutta, Figueira, and Das (2019) studied orness measure to define the or‐like degree of the Bonferroni mean and its variants. Considering non-additive value function, previous researchers constructed the optimization model using fuzzy measure to capture tolerance attitudes required from DMs without any post optimality analysis (Li et al., 2018, Yao et al., 2018). It is doubted that the decision from the inferred preference model is robust in case of the existence of the multiple feasible near-optimal solutions. As noted by Dujmović and Larsen (2007), the requirements (or expectations) on criteria combining by preference model cannot be neglected during preference aggregation. Subsequently, concise mathematical formulas for them (Hudec and Mesiar, 2020, Marichal, 2004) were defined to express a global or local tolerance degree of the DMs.

Conclusion

     In this paper, under a robust perspective, we present a tolerance framework to address a group multiple criteria ranking problem with indirect preference information such as the interaction, importance and tolerance of criteria as well as pairwise comparison among alternatives and criteria. Based on indirect preference information, Choquet integral preference model is elicited to capture the interaction, importance and tolerance of criteria for the final decision. With respect to the tolerance attitudes of DMs such as the mandatory/sufficient requirements on criteria for specific alternatives, they are quantified as tolerability constraints for preference elicitation. Once inconsistency issue (the feasibility of the whole preference constraints) in preference constructive learning process occurs, in the tolerance framework, two developed strategies including cause oriented strategy and consequence oriented strategy are applied. Then identify the minimal unsatisfied subsets of preference constraints responsible for the inconsistency or the maximum satisfied subsets of preference constraints as a specific set of consistent preference information by regression-based mixed 0-1 integer linear programs. By following the idea of treating all possible sets of preference parameters in line with indirect preference information, SMAA-like simulation algorithm is constructed to generate the rank result in the probabilistic form, which is used to generate a solution considered by exploring the whole instances of the compatible preference model. The proposed method can deal with indirect preference information involved with not only less cognitive effort but also the tolerance attitudes on criteria for the DMs, which has been paid insufficient attention in the literatures. From the illustration and application, it suggested that the proposed unified framework for MCDA has two characteristics: expressiveness of the underlying preference model which can reconstruct indirect preference information provided by the DMs and robustness of the final result which address uncertainties and imprecision observed in the actual decision support processes.

Abstract

     The modern approaches to modeling for creating new nickel-based superalloys are analyzed. The computer calculation methods used in domestic and foreign practice and based on multifactor design of experiments are presented

Introduction

     Currently, the active work on creating the fifthand sixth-generation aviation gas turbine engines, which is being performed in advanced states, is accompanied by vigorous development of effective methods to significantly facilitate and shorten the time of production of new high-temperature alloys, the difficulties of manufacture of which have increased many times due to the sharp complication of alloying. The regions of optimum alloy compositions began to be located near the phase boundaries of various undesirable or harmful phases.

     This problem is challenging due to the fact that the achieved complexity of alloying (primarily cast nickel superalloys) has led to the fact that their further development and optimization of the compositions of promising alloys are associated with ensuring simultaneous consideration of a significant number of the factors directly affecting their performance.

ANALYTICAL CALCULATIONS OF PHASE DIAGRAMS

     In the 1960s–1970s of the last century, the direction of calculating and predicting phase diagrams in materials science, which today has resulted in a rather powerful and popular direction, developed very actively. The problem of finding criteria for phase diagrams of different types has been solved since the time of van der Waals [4], who, working in the field of theoretical molecular physics, studied the behavior of molecules characterizing the state of material. In 1869, he discovered the forces of interaction between molecules, which were later called the van der Waals forces. In 1873, in his dissertation for Doctor of Philosophy, he developed a model, which uniformly describes the gaseous and liquid forms of matter, and derived an equation of state for gases and liquids. About this work, Maxwell said that it immediately put his name on a par with the most outstanding names in science.

Abstract

     DEM computer simulation was implemented to reveal the effects of different parameters on the efficiency of the roller screen. The numerical simulations were conducted under different device settings. In addition to the overall efficiency, further index was defined to determine the portion of cooperation of the rollers with different locations along the length of the device. It was revealed that the increase in the deck angle and rollers diameter reduced the overall efficiency while there was an optimum speed delivering the highest efficiency. It was concluded that the local cooperation share also depends on the introduced factors. In addition to the efficiency as an operational aspect, mechanical strength criteria were also considered to avoid probable device failure. Discussions were drawn concerning the obtained results and probable physical reasons of the obtained dependencies. The results can assist pelletizing factories to reduce their overall cost owing to increasing the process efficiency.

Introduction

     Iron and its alloys are the most important engineering materials utilized in industries. After the Second World War, the demand for all engineering materials was increased rapidly, with iron still ranking first. There are different approaches to obtaining metallic iron from iron ore concentrate including blast furnace and direct reduction. In the blast furnace technology, powdered iron ore concentrate with a size of tens of microns is charged into the furnace. On the other hand, in the reduction process the charge into the reactor is in the form of pellet. Via chemical reactions between the charged natural gas and the iron pellet in the reactor, oxygen is eliminated from either Hematite (

) or Magnetite (

) to obtain pure iron (

). Such process results in iron sponges suitable for subsequent melting and casting process. The diameter of the pellets should be in a certain range, preferably 8-16 mm, to have a successful reaction in the direct reduction process. Therefore, iron pellet is the main requirement for making sponge iron, and is made via the agglomeration of iron ore small particles in a rotating drum or rotating disk (flange on the rim). The agglomeration occurs due to different reasons including the surface tension of the water around the particles, adhesion of the binder, wan der Waals forces, magnetic force associated with the paramagnetic property of the particles and physical interlocking of the particles. The green pellets produced in the balling drum/disk are sent into the induration furnace to be heated up to 1300 °C and then cooled down to the ambient temperature. The objective of the present research is studying the screening process of the green pellets prior to feeding the induration furnace; thus, for more illustrations about other processes, interested readers can refer to relevant references such as Tupkary and Tupkary (2013).

Numerical results and discussion

     As depicted in Fig. 7 the pellets can be categorized into four groups including: ‘Much undersized’ that are much smaller than the gap between the rollers and creates the least trouble for passing through the gap and therefore this group takes critical degree of 1. The pellets that are much larger than the gap, named ‘Much oversized’, stay on top of the batch and do not remain inside the trap between the rollers and they can travel to the end of the screen with minor trouble and hence are categorized as critical degree of 2. The pellets that are slightly smaller than the gap named ‘Near undersized’ have a high probability of coming inside the trap between the rollers. They can pass through the gap although not easily and hence take critical degree of 3. The pellets a little larger than the gap named “Near oversized”, and create the most serious problem for the screen as they remain inside the trap between the rollers similar to a wedge causing blinding the gap. Therefore, the highest critical degree, 4, is assigned to this group of pellets.

Abstract

Document Sections

I. Introduction

II. Network Scenarios

III. Simulation Results and Discussion

IV. Conclusion

Authors

Figures

References

Keywords

Metrics

Abstract:

Network simulation is a tool used to analyze and predict the performance of Industrial Internet of Things deployments while dealing with the complexity of real testbeds. Large network deployments with complex protocols such as transmission control protocol are subject to chaos-theory behavior, i.e., small changes in the implementation of the protocol stack or simulator behavior may result in large differences in the performance results. In this article, we present the results of simulating two different scenarios using three simulators. The first scenario focuses on the Incast phenomenon in a local area network where sensor data are collected. The second scenario focuses on a congested link traversed by the collected measurements. The performance metrics obtained from the simulators are compared among them and with ground-truth obtained from real network experiments. The results demonstrate how subtle implementation differences in network simulators impact performance results, and how network engineers must consider these differences.

Introduction

The Industrial Internet of Things (IIoT) is adding large numbers of devices to communication networks. In this era, new devices are connected and old ones are removed, equipment from different vendors is added, old applications stop being used and new ones are developed, controller servers are moved from one switch to another or even to a remote site, new links with increased capacity are added, and new routing protocols are deployed, perhaps without removing old ones, all of this at a really fast pace. Any of these changes can yield consequences in network performance that are very difficult to predict. In complex systems, such as modern networks, because of nonlinearities and synchronizations, small changes can yield large-scale consequences. This phenomenon has been referred to as the amplification principle [1].

At present, it is unfeasible to predict network behavior and performance using analytical tools. This is because a large number of elements must be considered in the model, including nondeterministic behaviors (e.g., user actions). Researchers in academia have attempted approximations based on the analysis of complex systems [2] or chaotic systems [3]; however, the results are not applicable for problem solving in real networks, and engineering approximation continues to be employed in industry [4].

An alternative solution to analytical prediction is simulation modeling. New network or system configurations are tested before actual deployment using simulation software. A network administrator can predict the effects of moving localized controller servers to a cloud service by simulating the present environment and then implementing larger delay and loss parameters and network bottlenecks in the access links to simulate the future environment. New network topologies can be tested, and the results of link utilization can be evaluated. Depending on the complexity of the scenario or the expected changes, simple or complex models can be implemented in the simulation.

Highlights

Abstract

Graphical abstract

Abbreviations

Keywords

1. Introduction

2. State of the art

3. Technological opportunities and challenges

4. Educational implications

5. Economic and social challenges

6. Conclusion and future directions

Declaration of Competing Interest

Acknowledgements

References

Abstract

With the advent of digitalization and industry 4.0, education in chemical and biochemical engineering has undergone significant revamping over the last two decades. However, undergraduate students sometimes do lack industrial exposure and are unable to visualise the complexity of actual process plants. Thereby, students might graduate without adequate professional hands-on experience. Similarly, in the process industry, operator training-simulators are widely used for the training of new and skilled operators. However, conventional training-simulators often fail to simulate reality and do not provide the user with the opportunity to experience unexpected and hazardous scenarios. In these regards, virtual reality appears to be a promising technology that can cater to the needs of both academia and industry. This paper discusses the opportunities and challenges for the incorporation of virtual reality into chemical and biochemical engineering education with an emphasis on the fundamental areas of technology, pedagogy and socio-economics. The paper emphasises the need for augmenting virtual reality interfaces with mathematical models to develop advanced immersive learning applications. Further, the paper stresses upon the need for novel educational impact assessment methodologies for the evaluation of virtual-reality-based learning. Finally, an ongoing case study application is presented to briefly discuss the social and economic implications, and to identify the bottlenecks involved in the adoption of virtual reality tools across chemical and biochemical engineering education.

1. Introduction

With rapid advances in technology, digital solutions are being widely embraced for teaching and for learning purposes (Quintero et al., 2019; Radianti et al., 2020). This includes the use of online learning platforms, interactive digital applications, and collaborative environments in education. Furthermore, the easy availability of portable digital devices including notebooks and smartphones fostered the digital embracement for education. Recently, immersive virtual environments are also being considered as a viable option for education and training owing to the availability, affordability, and rapid advancements in these technologies (Akkoyun, 2017; Jensen and Konradsen, 2018).

In academia, growing student numbers combined with the limited availability of physical resources is leading educational institutions to explore novel teaching alternatives that are, primarily, cost-effective in nature. Moreover, force-majeure situations such as the COVID-19 pandemic advocates for more educational robustness and distance learning options. Similarly, in industry, with increased automation and complexity of modern process plants, advanced training methods are becoming more desirable for the effective training of operators. In this regard, three-dimensional (3D) virtual reality environments, similar to those being used in the gaming and film industry, can be effectively utilised to generate learning environments (Schofield, 2012). Even though virtual reality is not a new technology, recent advancements have aroused increased interest among scholars (Akbulut et al., 2018). With the arrival of low-budget head-mounted displays (HMDs), immersive virtual environments are becoming more accessible for the purpose of education (Jensen and Konradsen, 2018).

Highlights

Abstract

Keywords

Nomenclature

1. Introduction

2. The proposed SA-MODDE algorithm

3. Numerical experiments

4. Chemical engineering processes optimization

5. Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

Appendix A. Supplementary data

References

Abstract

This paper proposes a new multi-objective dynamic differential evolution algorithm with parameter self-adaptive strategies, named SA-MODDE. All components of the algorithm are synergically designed to reach its full potential, containing parental selection, mutation strategy, parameter setting, survival selection, constraint handling, and termination criteria. The improvement measures emphasize exploiting Pareto dominance information more efficiently. Particularly, parameter adaptation schemes are introduced based on both prior knowledges of current individual and feedback information on previous promising solutions, and their effectiveness is validated by comparison with three fixed-parameter combinations. Extensive numerical tests are conducted on multiple test suites with five state-of-the-art peer competitors. The statistical results demonstrated that the SA-MODDE exhibits good proximity and diversity in dealing with benchmark functions with various characteristics. Three industrial (bio)chemical processes, including two optimal control and one reformulated constrained tri-objective, are investigated to show the feasibility and robustness of the SA-MODDE.

1. Introduction

Engineering problems always require the simultaneous optimization of several competing objectives of interests. So far, multi-objective optimization (MOO) has been an active research field in process systems engineering [1,2]. Particularly, various multi-objective evolutionary algorithms (MOEAs), such as NSGAII, GDE3, and MOPSO, have been widely used to solve both academic and industrial MOO problems [3–5]. Usually, MOEAs have two main advantages: (1) As many diverse non-dominated solutions as possible can be found in a single run; (2) Various types of MOO problems can be handled without assumptions on objective functions and their mathematical characteristics [6].

The algorithm structure and search operator jointly affect the performance of MOEAs. The algorithm structure can be classified into two main categories: Pareto-based [3,7] and decompositionbased [8,9]. The former provides detailed Pareto dominance information of the population to facilitate individual comparison. The latter decomposes MOO problems into a set of scalar aggregation subproblems, each of which is optimized using the current information from neighboring subproblems. The two methods have their own advantages on different types of problems and are considered to be evenly matched [10]. In terms of search operator, differential evolution (DE) is simple to implement with only a few control parameters, i.e., scale factor (F ) and crossover rate (CR). Except for multi-objective DE (MODE) algorithms, many classic MOEAs also replaced the original evolutionary operators with DE and their performance was significantly enhanced, such as NSGAII-DE, SPEA2-DE, IBEA-DE, and MOEA/D-DE [11,12]. Through updates by dynamic population rather than generation to generation, Qing [13] presented the dynamic DE (DDE) operator, superior in efficiency, robustness, and storage requirements to the conventional DE. That is, each new individual that performs better than or similar to the corresponding old counterpart will immediately participate in the current population to provide information for subsequent evolution. This makes DDE more responsive to changes in population status. Despite of researches on multi-objective DDE (MODDE) algorithms [14,15], it is still very inadequate compared to MODE. Herein, we propose several improvement measures on the MODDE under Pareto-based structure. For convenience, the background of MOO problems and DDE operators are given in Supplementary Materials.

1- Introduction

2- Literature review

3- Research method

4- Findings

5- Discussion and conclusions

References

Abstract

This paper seeks to determine whether international marketing simulations provide an authentic assessment of learning. The principles of authentic assessment dictate that assigned learning activities be aligned with the attitudes, skills and knowledge that students will be required to demonstrate in the real world. Research on the application of authentic assessment principles in management education is limited and most of the works that have examined the issue have done so from the perspective of the educator, not the student. A content analysis was undertaken of 122 final reports submitted by teams participating in an online international marketing simulation. The results demonstrate that the simulation provided students with opportunities for reflection and the development of an understanding of the real world of international marketing, with all its complexities and challenges. The simulation also allowed students to receive feedback, correct mistakes and gain an appreciation of the varied activities that contribute to the achievement of an overall objective. Students did not, however, appreciate the transferability of skills acquired in the simulation to other domains of knowledge. Similarly, the final reports did not reflect a significant appreciation of the communication and collaboration benefits that simulations should provide. Further, provision of instructional support was shown to have no impact on students’ perceptions of the simulation’s authenticity.

Introduction

This paper seeks to determine whether computer simulations provide undergraduate business students with an authentic assessment of their capabilities in international marketing. Authentic assessment is generally viewed as the constructive alignment of curriculum and the associated assessments where assigned activities allow students to develop skills viewed as central to the real world (Ashford-Rowe, Herrington, & Brown, 2014; James & Casidy, 2018). Students are required to demonstrate the application of these skills through the successful completion of assignments which mimic tasks they would be required to perform in their careers. The topic of authentic assessment has garnered considerable interest in recent years as educators search for new approaches to close the gap between the expectations of employers and the skills imparted to students (Gault, Leach, & Duey, 2010). In a highly competitive job market employers are more inclined to commit to graduates who are able to immediately contribute to the corporate mission and require minimal orientation to the requirements of the real world. While authentic assessments focus on higher order thinking skills and the completion of multiple tasks, traditional, i.e. inauthentic assessments, are more concerned with rote memorization of facts and figures and the successful completion of standardized, closed book, multiple choice exams (James & Casidy, 2018).

1- Introduction

2- System architecture

3- Modelling of the WECS

4- Performance analysis of proposed DC nanogrid topology

5- Conclusion

References

Abstract

Wind energy conversion system (WECS) based DC nanogrid topology is proposed in this paper. The WECS topology is based on self-excited induction generator (SEIG) which is cost effective, and has rugged construction and combination of diode bridge rectifier (DBR) with DC-DC converter to implement the maximum power point tracking technique. The combination of diode DBR with DC-DC converter allows simple control structure due to one controllable switch as compared to complex control in the conventional controlled/semi-controlled rectifier with six controllable switches. Further, proposed DC nanogrid topology enhances the SEIG efficiency due to the reduction of harmonic injection on machine side from DBR implementation. The detailed harmonic spectrum analysis is carried out by developing the laboratory scale prototype of DBR based SEIG topology for WECS to justify the selection of DBR instead of the controlled rectifier. Further, to analyze the effect of different types DC-DC converter to make combination with the DBR. Afterwards, the dynamic performance of the proposed DC nanogrid topology is analyzed to understand the selection of the DC-DC converter with DBR for different DC grid voltage conditions.

Introduction

Electricity plays a vital role in energy supply, however, the electricity generation is the single largest source of CO2 emission, which is more than 40% of CO2 emission related to global energy. By the year 2030, global electricity demand is likely to double [1,2]. Hence, climate change mitigation and increasing energy demand motivates for the development of renewable energy resources. The renewable energy resources such as solar and wind energy are distributed in nature which favors the electricity generation and consumption on site. With technological advancement and regulatory changes, the concept of supplying the load locally has led to a concept of nanogrid. Thus, the development of renewable based nanogrid are gaining attention in recent past for providing environment-friendly, sustainable and economical electricity [3]. Further, the development of power electronics has transformed the load profile of end user as the modern appliances operate on DC supply [4]. Having this understanding, the concept of DC nanogrid has been proposed in the literature to elude multiple AC-DC conversion and vice-versa [5]. The DC distribution enables easy control, no issue of frequency, power angle, phase sequence, and power factor. Mashood et al.

1- Introduction

2- Method and model

3- Results

4- Discussion

5- Conclusions

References

Abstract

The XiLuoDu reservoir dam located on the Jinsha River is one of the largest dams in the world. Nearby faults are very active, and two M > 7 strong earthquakes have occurred around the dam. Simulating the rupture dynamics of potential earthquakes on the surrounding faults is very important to estimate the seismic hazard for the dam. In this research, we use the curved grid finite difference method (CG-FDM) to simulate spontaneous ruptures on the major faults around the dam. The main results are that the Leibo middle and south faults (LBMF and LBSF) are more likely to have large destructive earthquakes than the Mabian-Yanjin east fault (MB-YJEF). Asperities on faults that have an impact on strong ground motion are formed under the combined influence of the regional background stress field and fault geometry. Spontaneous ruptures on the LBMF and LBSF are mainly strike-slip with minor thrust components, while those on the MB-YJEF are strike-slip and thrust; these results are consistent with the focal mechanisms of recorded small and moderate earthquakes in this area.

Introduction

The Jinsha River , with a total length of 3364 km, is located in the upper reaches of the Yangtze River in China and is rich in water energy resources. There are four world -class reservoir dams on its lower reaches . The XiLuoDu reservoir is located in the Jinsha River gorge section between Leibo County in Sichuan Province and Yongshan County in Yunnan Province; it has a total reservoir capacity of 13.6 billion m3 and a maximum dam height of 278 m (https://www.ctg.com.cn/sxjt/zt98/cyjsj/xwtqh/313438/index.html). This reservoir is a large -scale project that is useful for power generation, flood control, sediment retention and improvement of downstream shipping conditions, but the reservoir is located within the Daliangshan sub -block at the junction of the Qinghai -Tibetan block and the South China block (Deng et al., 2003; Diao et al., 2014; Zhang et al., 2003 ). The seismicity and earthquake intensity are both very strong in this area. The seismic resistance of the reservoir dam is very important (https://www.ctg.com.cn/sxjt/zt98/cyjsj/xwtqh/313438/index.html), and a more accurate estimation of earthquake disasters in the dam site area is urgently needed. Some faults are active around the dam. The Lianfeng fault (LFF) and many small faults in the Mabian -Yanjin fault zone are mainly distributed within 20 -30 km of the XiLuoDu dam, forming a nearly triangular small block called the Leibo -Yongshan triangular block.

The high-redundancy actuator (HRA) is a linear displacement actuation system, which consists of a large number of small electromechanical actuation elements arranged in a series and parallel configuration. The performance of a HRA with 9 direct-driven linear electromechanical actuators was analyzed under different fault conditions with the aid of the MATLAB simulation model. The results showed that the HRA has the capability to provide inherent fault-tolerance. Hence, the sudden failure of the system in the presence of fault was avoided. However, the capability of the actuator was found to be reduced gradually depending upon the number of faults.

Introduction

Faulty elements in any engineering system can be a huge hassle. Sometimes, they might cause irrevocable damage to the industries. Particularly, a fault in the safety-critical system can lead to disasters of unimaginable magnitude. So, designing modern engineering systems with a high level of reliability is of utmost importance to avoid any unexpected failures [1]. One important way this can be prevented is by having a fault-tolerance design that can tolerate any malfunctions of the individual components of the system. In general, fault-tolerance can be achieved by adopting a redundancy technique which includes the addition of resources, time or information beyond the normal requirements of the system being operated. In recent years, fault-tolerance has become one of the greatest tools for providing safety to any mechatronic system. The aircraft actuation and nuclear power systems are some of the most important systems that need redundancy to ensure reliability [2]. More recently, the aerospace field significantly increased the use of multi redundant electrical actuation systems to avoid unexpected failures [3,4]. However, in the case of nuclear power systems, hardware redundancy is introduced to confirm a high level of reliability of the control system [5]. Traditionally, the fault-tolerance for the actuation system is attained by connecting two or more identical actuators in parallel and the task to be completed by this group of actuators in parallel, is carried out by any one of the individual actuators from the same group. The fact that each of these actuators has the capability to perform the required task alone enables them to override any faulty actuators [6]. However, these actuators in parallel increases the weight and cost of the system and eventually, reduces the efficiency of the system. Moreover, the use of pure parallel configuration holds no value in the event of jamming/lock-up of an actuator within it.

1- Introduction

2- Problem statement

3- Methodology

4- Case studies

5- Conclusions

References

Abstract

Flare is the last safety measure for daily operations in oil, gas & chemical process industries (OGCPI). However, an excessive flaring releases large quantity of emissions of VOCs and NOx, which may suddenly enhance local ozone as a secondary pollution. Normally, the flare destruction and removal efficiency (DRE) of 98% or 99% is regulated as the national standard and presumed for industrial practices in the U.S. Unfortunately, real DRE values could be much lower than the standard due to impact factors including various meteorological and operating conditions such as the cross-wind speed, flare jet velocity and heating value of combustion. Thus, it is critically important to explore the sensitivity of the regional ozone impact due to low DREs of OGCPI flare combustions. In this paper, a systematic methodology has been developed to examine ozone impacts due to the low flare DREs, which have never been systematically studied before. The DRE formulas were derived from computational fluid dynamic (CFD) modeling and Water Environment Research Foundation (WERF) results and then employed to recompile the point source emission inventory. After that, comprehensive air quality model with extensions (CAMx) was employede to simulate and quantify local ozone changes impacted by flare emissions of OGCPI. Case studies indicate that the maximum hourly ozone increments due to the low DRE through CFD and WERF modeling is 0.18 ppb and 1.3 ppb, respectively. This study could enrich fundamental understandings of industrial point source emissions and provide the quantitative and valuable support for the ozone pollution caused by OGCPI flare emissions under low DRE instead of standard values.

Introduction

Industrial flaring is to safely combust off-spec, unusable, or unwanted process streams, which might otherwise be harmful to local environment if directly vented without destructions. The oil, gas and chemical process industries (OGCPI) in the U.S. daily processes millions of cubic feet of hydrocarbon gases (Baukal and Schwartz, 2001; Aalsalem et al., 2018). Thus, a slight decrease in flaring performance will release millions of cubic feet of gaseous emissions into the atmospheric environment. Note that although flaring is a safety measure for plant safety in OGCPI, excessive flaring will generate large amounts of emissions such as NOx (nitrogen oxides), CO2, CO, VOCs (volatile organic compounds) especially for high reactive VOCs (i.e., HRVOCs such as ethylene, propylene, acetylene). For instance, an olefin plant with a capacity of 1.2 billion pounds of ethylene productivity per year can easily flare about 5.0 million pounds of ethylene during one single start-up 50 operation (Xu and Li, 2008).