scholarly journals A Systematic Disturbance Analysis Method for Resilience Evaluation: A Case Study in Material Handling Systems

2019 ◽  
Vol 11 (5) ◽  
pp. 1447 ◽  
Author(s):  
Ruiying Li ◽  
Xiaoyu Tian ◽  
Li Yu ◽  
Rui Kang
Keyword(s):  
Material Handling ◽  
Evaluation Method ◽  
Intelligent Manufacturing ◽  
Evaluation Framework ◽  
System Response ◽  
Key Performance Index ◽  
The Monte Carlo Method ◽  
Disturbance Analysis ◽  
System Resilience

With the development of intelligent manufacturing technology, the material handling system (MHS) faces larger resilience challenges that threaten the sustainability of the system. To evaluate system resilience, the disturbance that the system may experience and the system response need to be identified in advance. This paper proposes a systematic and innovative approach to performing resilience-related disturbance analysis, i.e., disturbance mode and effects analysis (DMEA). Using this method, the possible disturbance modes, their occurrence probabilities, and the quantitative effects on system performance can be collected in a bottom-up process, and the information can be applied to further resilience quantification. Moreover, a quantitative system resilience evaluation framework for the MHS based on DMEA and the Monte Carlo method is presented. Production is defined as the key performance index of the system and is monitored to reflect the resilience behavior of the system after the disturbance occurs. The resilience of a tire tread handing system is quantified in our case study, and the results show the effectiveness of our DMEA-based resilience evaluation method. We also find that a reasonable system configuration and maintenance strategy can effectively improve system resilience, and a trade-off can be made between resilience and cost.

scholarly journals A virtual hydrological framework for evaluation of stochastic rainfall models

2018 ◽  
Author(s):  
Bree Bennett ◽  
Mark Thyer ◽  
Michael Leonard ◽  
Martin Lambert ◽  
Bryson Bates
Keyword(s):  
Evaluation Method ◽  
South Australia ◽  
Evaluation Methods ◽  
Evaluation Framework ◽  
Streamflow Prediction ◽  
Direct Evaluation ◽  
Rainfall Model ◽  
Rainfall Statistics ◽  
The Impact

Abstract. Stochastic rainfall modelling is a commonly used technique for evaluating the impact of flooding, drought or climate change in a catchment. While considerable attention is given to the development of stochastic rainfall models, significantly less attention is given to performance evaluation methods. Typical evaluation methods employ a variety of rainfall statistics. However, they give limited understanding about which rainfall characteristics are most important for reliable streamflow prediction whenever the simulated rainfall are poor. To address this issue a new evaluation method for rainfall models is introduced, with three key features: (i) streamflow-based – to give a direct evaluation of modelled streamflow performance, (ii) virtual – to avoid the issue of confounding errors in hydrological models or data, and (iii) targeted – to isolate the source of errors according to specific sites and months. The virtual hydrologic evaluation framework is applied to a case study of 22 sites in South Australia. The framework demonstrated that apparently good modelled rainfall can produce poor streamflow predictions, whilst poor modelled rainfall may lead to good streamflow predictions, as catchment processes can dampen or amplify rainfall errors when converted to streamflow. The framework identified the importance of rainfall in the wetting-up months of the catchment cycle (May and June in this case study) for providing reliable predictions of streamflow over the entire year despite their low monthly flow volume. This insight would not have been found using existing methods and highlights the importance of the virtual hydrological evaluation framework for stochastic rainfall model evaluation.

Sugar factory audits, debottlenecking and efficiency improvement

2020 ◽  
pp. 607-612
Author(s):  
Bernard Coûteaux
Keyword(s):  
Evaluation Method ◽  
Predictive Modelling ◽  
Cane Sugar ◽  
Sugar Factory ◽  
Low Energy Consumption ◽  
Process Simulations ◽  
Capital Efficiency ◽  
Potential Improvement ◽  
Primary Focus

This paper elaborates on the key solutions offered by De Smet Engineers & Contractors (DSEC) to optimize the efficiency of cane sugar producing and processing facilities. In order to meet customer needs, DSEC offers proprietary predictive models built using the latest versions of specialized software. These models allow factory managers to envision the whole picture of increased operational and capital efficiency before it becomes reality. An integrated energy model and the CAPEX/OPEX evaluation method are discussed as ways to estimate and optimize costs, both for new greenfield projects and revamping of existing factories. The models demonstrate that factory capacities can be successfully increased using equipment that is already available. Special attention is paid to crystallization and centrifugation process simulations and the potential improvement of the global energy balance. One case study shows the transformation of a beet sugar factory into a refinery to process raw cane sugar after beet crop season and the second case shows the integration of a refinery into a cane sugar factory. The primary focus of the article is optimization of the technological process through predictive modelling. DSEC’s suggested solutions, which lead to great improvements in a plant’s efficiency and its ability to obtain very low energy consumption, are discussed.

The Rapid Iterative Test and Evaluation Method (RITE)

2018 ◽  
Author(s):  
Michael C. Medlock
Keyword(s):  
Evaluation Method ◽  
Test And Evaluation ◽  
Original Case ◽  
Definition Of

This chapter begins with a discussion of the philosophy and then definition of the RITE method. It then delves into the benefits of this method and provides practical notes on running RITE tests effectively. The chapter concludes with an overview of the original case study behind the 2002 article documenting this method.

A framework for modeling and assessing system resilience using a Bayesian network: A case study of an interdependent electrical infrastructure system

2019 ◽  
Vol 25 ◽  
pp. 62-83 ◽  
Author(s):  
Niamat Ullah Ibne Hossain ◽  
Raed Jaradat ◽  
Seyedmohsen Hosseini ◽  
Mohammad Marufuzzaman ◽  
Randy K. Buchanan
Keyword(s):  
Bayesian Network ◽  
Infrastructure System ◽  
System Resilience

scholarly journals Evaluating process-based integrated assessment models of climate change mitigation

2021 ◽  
Vol 166 (1-2) ◽  
Author(s):  
Charlie Wilson ◽  
Céline Guivarch ◽  
Elmar Kriegler ◽  
Bas van Ruijven ◽  
Detlef P. van Vuuren ◽  
...  
Keyword(s):  
Climate Change ◽  
Integrated Assessment ◽  
Climate Change Mitigation ◽  
Evaluation Method ◽  
Evaluation Research ◽  
Evaluation Framework ◽  
Systematic Evaluation ◽  
Integrated Assessment Models ◽  
Wide Range ◽  
Change Mitigation

AbstractProcess-based integrated assessment models (IAMs) project long-term transformation pathways in energy and land-use systems under what-if assumptions. IAM evaluation is necessary to improve the models’ usefulness as scientific tools applicable in the complex and contested domain of climate change mitigation. We contribute the first comprehensive synthesis of process-based IAM evaluation research, drawing on a wide range of examples across six different evaluation methods including historical simulations, stylised facts, and model diagnostics. For each evaluation method, we identify progress and milestones to date, and draw out lessons learnt as well as challenges remaining. We find that each evaluation method has distinctive strengths, as well as constraints on its application. We use these insights to propose a systematic evaluation framework combining multiple methods to establish the appropriateness, interpretability, credibility, and relevance of process-based IAMs as useful scientific tools for informing climate policy. We also set out a programme of evaluation research to be mainstreamed both within and outside the IAM community.

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