Design for an Intelligent Waste Classifying System: A Case Study of Plastic Bottles

The use of plastic bottles has become a significant environmental concern, and recycling them has become a priority. Small and medium-sized recycling companies must collect and categorize large volumes of plastic bottles and sell them to larger recycling firms, a process that is time-consuming, costly, and labor-intensive. This manual sorting process can pose health risks, particularly during the COVID-19 pandemic, and can affect worker productivity. To address these issues, this study proposes the development of an automated conveyor belt system that can rapidly and accurately separate plastic bottles by type. The system utilizes an opaque and transparent plastic bottle separation platform, which saves time, cost, and manpower. This system design provides recycling SMEs with a competitive advantage by serving as a practical application model and a prototype with an easy-to-use concept. Key tools employed in this research include product design development (PDD), Kansei engineering, manufacturing process design, controlling system, and fault tree analysis (FTA). The light sensors are critical components in the separation process, detecting the opacity or transparency of the bottles' surfaces. The proposed prototype's reliability will be assessed by FTA, which considers all potential failures. This study contributes to the body of knowledge surrounding the integration of conveyor systems and provides valuable information for businesses seeking to optimize their sorting processes. The guidelines developed in this study can serve as a starting point for further research on the integration of conveyors in waste sorting plants. © 2013 IEEE.

A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy Environment

Reliability of high demand machines is quite necessary and it can be maintained through proper and timely maintenance, Ultra-low temperature (ULT) freezer is one of those kinds of machines which are in high demand during covid-19 pandemic for the storage of vaccine. The rapid production of vaccines for the prevention of coronavirus disease 2019 (COVID-19) is a worldwide requirement. Now the next challenge is to store the vaccine in a ULT freezer. It's become really a big problem to store the vaccine which creates the demand of ULT freezer. The present paper investigates a situational based performance of the ULT freezer with the aim to predict the impact of different component failures as well as human errors on the final performance of the same. For the study, it is not possible to extract the parameters (failure rate and repair time) of the components that never failed before. Thus, to overcome this difficulty, here authors use the possibility theory. Authors present the available data in Right triangular fuzzy number with some tolerance as suggested by system analyst. The lambda-tau methodology and arithmetic operations on right triangular generalized fuzzy numbers (RTrFN) are used to find the various performance parameters namely MTTF, MTTR, MTBF, reliability, availability, maintainability (RAM) and ENOF, under fuzzy environment. The proposed model has been studied using possibility theory under working conditions, preventive maintenance as well as under the rest of conditions. This study reveals the most and least critical component of the ULT freezer which helps maintenance department to plan the maintenance strategy accordingly.

PFL: A Probabilistic Logic for Fault Trees

Safety-critical infrastructures must operate in a safe and reliable way. Fault tree analysis is a widespread method used for risk assessment of these systems: fault trees (FTs) are required by, e.g., the Federal Aviation Administration and the Nuclear Regulatory Commission. In spite of their popularity, little work has been done on formulating structural queries about and analyzing these, e.g., when evaluating potential scenarios, and to give practitioners instruments to formulate queries on in an understandable yet powerful way. In this paper, we aim to fill this gap by extending [37], a logic that reasons about Boolean. To do so, we introduce a Probabilistic Fault tree Logic is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties that comprise probabilities. Alongside, we present, a domain specific language to further ease property specification. We showcase and by applying them to a COVID-19 related FT and to a FT for an oil/gas pipeline. Finally, we present theory and model checking algorithms based on binary decision diagrams (BDDs). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PFL: A Probabilistic Logic for Fault Trees

Safety-critical infrastructures must operate in a safe and reliable way. Fault tree analysis is a widespread method used for risk assessment of these systems: fault trees (FTs) are required by, e.g., the Federal Aviation Administration and the Nuclear Regulatory Commission. In spite of their popularity, little work has been done on formulating structural queries about and analyzing these, e.g., when evaluating potential scenarios, and to give practitioners instruments to formulate queries on in an understandable yet powerful way. In this paper, we aim to fill this gap by extending [37], a logic that reasons about Boolean. To do so, we introduce a Probabilistic Fault tree Logic is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties that comprise probabilities. Alongside, we present, a domain specific language to further ease property specification. We showcase and by applying them to a COVID-19 related FT and to a FT for an oil/gas pipeline. Finally, we present theory and model checking algorithms based on binary decision diagrams (BDDs). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

BFL: a Logic to Reason about Fault Trees

Safety-critical infrastructures must operate safely and reliably. Fault tree analysis is a widespread method used to assess risks in these systems: fault trees (FTs) are required-among others-by the Federal Aviation Authority, the Nuclear Regulatory Commission, in the ISO26262 standard for autonomous driving and for software development in aerospace systems. Although popular both in industry and academia, FTs lack a systematic way to formulate powerful and understandable analysis queries. In this paper, we aim to fill this gap and introduce Boolean Fault tree Logic (BFL), a logic to reason about FTs. BFL is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties. Alongside BFL, we present model checking algorithms based on binary decision diagrams (BDDs) to analyse specified properties in BFL, patterns and an algorithm to construct counterexamples. Finally, we propose a case-study application of BFL by analysing a COVID-19related FT. © 2022 IEEE.

Exploring Critical Events of Perishable Product Supply Chain Using Fault Tree Methodology to Deal with Impacts of COVID-19

This research investigates the impact of pandemic COVID-19, on the perishable product supply chain (PPSC). Thematic analysis for the cause of failure in PPSC has been identified through the NVivo application. It examines the events that cause disruption. Secondly, fault tree methodology has adopted qualitative evaluation using the minimum cut set analysis and importance measures. A case study of the apple supply chain in Shimla, India has been included, collecting data from respondents, research papers, government reports, and newspaper articles published from the period March 2020 to December 2020. The occurrence of failure in the apple supply chain included crop yield loss, unavailability, and inaccessibility of apple products. After analysis, 13 minimum cut sets are obtained. These include critical failure event as: assistance in failure from government and organization, high food prices, labour shortage, and cross border restriction. Potential strategies for resilient PPSC have been proposed for an efficient decision-making process. © 2021 IEEE.

A QUANTITATIVE APPROACH TO ASSESS THE LIKELIHOOD OF SUPPLY CHAIN SHORTAGES

We define supply chains (SCs) as sequences of processes that link the demand and supply of goods or services within a network. SCs are prone to shortages in delivering their output goals due to several factors such as personnel undersupply, inefficient processes, policy failure, equipment malfunction, natural hazards, pandemic outbreaks, power outages, or economic crises. Recent notable supply-chain failures include the 2021 Texas power crisis, personal protection equipment shortages during the COVID-19 pandemic, and regional or global food chain shortages. The consequences of such shortages can range from negligible to devastating. The Texas power crisis resulted in the death of 70 people and left approximately 4.5 billion homes and businesses without power for multiple days. In this paper, we presented a methodology to quantify the failure probability of the throughput of a supply chain. We divided the methodology into two major categories of steps. In the first step, we converted the given or assumed supply chain data into fault trees and quantify them. In the second step, we iterated the quantification of the fault tree to build a supply chain shortage risk profile. We introduced the notion of success criteria for the output from a facility, based on which we included or excluded the facility for quantification. With the inclusion of relevant field data, we believe that our methodology can enable the stakeholders in the supply-chain decision-making process to detect vulnerable facilities and risk-inform prevention and mitigation actions. Applications for this methodology can include construction, inventory stocking, assessing manufacturing quantities, policy changes, personnel allocation, and financial investment for critical industries such as nuclear, pharmaceutical, aviation, etc. Copyright © 2021 by ASME

Analysis of the impact of a pandemic on the control of the process safety risk in major hazards industries using a Fault Tree Analysis approach.

The control of the risks associated with major hazard events is critical to the safe and continuous operation of the process industry. Over the last decades, the process industry has been successful at establishing and implementing robust Process Safety Management (PSM) systems to prevent and mitigate the consequences of such major hazard events. While there exist some industry guidelines developed relatively recently for events initiated by natural disasters and security-related threats, for initiating events like outbreaks of pathogens and pandemics, there is currently a clear lack of understanding of the impact of the restrictions and disruption caused by a pandemic on the ability of companies operating major hazard facilities to keep controlling the risks associated to their hazardous operations. Moreover, there is no industry guideline on how to account for such an impact in PSM systems for process safety hazards. The recent COVID-19 outbreak caused serious disruptions to normal operations that have challenged industry in their ability to control risks. The objective of this paper is to perform an analysis of the impact of a pandemic situation on the implementation of selected elements of PSM systems related to the identification and evaluation of the risks of a major hazard and their control. The approach chosen involves the analysis of the root causes of the failure of the selected PSM elements using a Fault Tree Analysis method. The findings provide the first steps in the establishment of recommendations for the upgrade of PSM systems to face events such as pandemics.

Methodology for risk assessment of COVID-19 pandemic propagation.

This paper proposes a methodology to perform risk analysis of the virus spread. It is based on the coupling between CFD modelling of bioaerosol dispersion to the calculation of probability of contact events. CFD model of near-field sneeze droplets dispersion is developed to build the SARS-CoV-2 effect zones and to adequately capture the safe distance. The most shared classification of droplets size distribution of sneezes was used. Droplets were modeled through additive heating/evaporation/boiling laws and their impact on the continuous phase was examined. Larger droplets move behind the droplet nuclei front and exhibit greater vertical drop due to the effect of gravity. CFD simulations provided the iso-risk curves extension (i.e., the maximum distance as well as the angle) enclosed by the incident outcome effect zone. To calculate the risk indexes, a fault tree was developed and the probability of transmission assuming as of the top event "COVID-19 infection" was calculated starting from the virus spread curve, as main base case. Four phases of virus spread evolution were identified: initiation, propagation, generalised propagation and termination. For each phase, the maximum allowable close contact was computed, being fixed the values of the acceptable risk index. In particular, it was found that during the propagation case, the maximum allowable close contacts is two, suggesting that at this point lockdown should be activated. The here developed methodology could drive policy containment design to curb spread COVID-19 infection.