Case Study:Identification of Microbial Distribution and Contamination Source by PDCA Cycle at a Boiled Noodles Factory

2021 ◽  
Vol 62 (3) ◽  
pp. 79-84
Author(s):  
Noboru Ohyagi ◽  
Takayuki Morita ◽  
Yoshiko Sugita-Konishi
Keyword(s):  
Contamination Source ◽  
Microbial Distribution ◽  
Pdca Cycle

Contamination Troubleshooting Approach from Problem to Solution

2019 ◽  
Author(s):  
Victor K. F. Chia ◽  
Hugh E. Gotts ◽  
Fuhe Li ◽  
Mark Camenzind
Keyword(s):  
Case Studies ◽  
Semiconductor Devices ◽  
Analytical Techniques ◽  
General Knowledge ◽  
Contamination Source ◽  
Root Cause ◽  
Sources Of Contamination

Abstract Semiconductor devices are sensitive to contamination that can cause product defects and product rejects. There are many possible types and sources of contamination. Root cause resolution of the contamination source can improve yield. The purpose of contamination troubleshooting is to identify and eliminate major yield limiters. This requires the use of a variety of analytical techniques[1]. Most important, it requires an understanding of the principle of contamination troubleshooting and general knowledge of analytical tests. This paper describes a contamination troubleshooting approach with case studies as examples of its application.

ANÁLISE DA APLICAÇÃO DO CICLO PDCA EM PROCESSOS DE CAPTAÇÃO E RETENÇÃO DOS ALUNOS EM UMA FACULDADE DE MANAUS/ANALYSIS OF THE APPLICATION OF THE PDCA CYCLE IN PROCESSES OF CAPTURE AND RETENTION OF STUDENTS IN A FACULTY OF MANAUS

2020 ◽  
Vol 6 (9) ◽  
pp. 72070-72084
Author(s):  
Priscilla Mendonça de Lima Melgueiro ◽  
Sil Franciley dos Santos Quaresma ◽  
Kleber Bittencourt Oliveira ◽  
Edílson Marques Magalhães
Keyword(s):  
Pdca Cycle

Dynamic changes in spatial microbial distribution in mixed-population biofilms: experimental results and model simulation

1995 ◽  
Vol 32 (8) ◽  
pp. 67-74 ◽  
Author(s):  
Satoshi Okabe ◽  
Kikuko Hirata ◽  
Yoshimasa Watanabe
Keyword(s):  
Oxygen Diffusion ◽  
Loading Rate ◽  
Model Simulation ◽  
Mixed Population ◽  
Experimental Results ◽  
Synthetic Wastewater ◽  
Diffusion Limitation ◽  
Dynamic Changes ◽  
Microbial Distribution ◽  
Rotating Biological Contactors

Dynamic changes in spatial microbial distribution in mixed-population biofilms were experimentally determined using a microslicer technique and simulated by a biofilm accumulation model (BAM). Experimental results were compared with the model simulation. The biofilms cultured in partially submerged rotating biological contactors (RBC) with synthetic wastewater were used as test materials. Experimental results showed that an increase of substrate loading rate (i.e., organic carbon and NH4-N) resulted in the microbial stratification in the biofilms. Heterotrophs defeated nitrifiers and dominated in the outer biofilm, whereas nitrifiers were diluted out in the outer biofilm and forced into the inner biofilm. At higher organic loading rates, a stronger stratified microbial spatial distribution was observed, which imposed a severe internal oxygen diffusion limitation on nitrifiers and resulted in the deterioration of nitrification efficiency. Model simulations described a general trend of the stratified biofilm structure. However, the actual stratification was stronger than the simulated results. For implication in the reactor design, when the specific carbon loading rate exceeds a certain limit, nitrification will be deteriorated or require a long start-up period due to the interspecies competition resulting in oxygen diffusion limitation. The extend of microbial stratification in the biofilm is especially important for determination of feasibility of nitrification in the presence of organic matters.

Characteristics of nitrogen removal and microbial distribution by application of spent sulfidic caustic in pilot scale wastewater treatment plant

2010 ◽  
Vol 62 (8) ◽  
pp. 1965-1965
Author(s):  
S. Park ◽  
J. Lee ◽  
J. Park ◽  
I. Byun ◽  
T. Park ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Nitrogen Removal ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Pilot Scale ◽  
Microbial Distribution ◽  
Spent Sulfidic Caustic ◽  
Sulfidic Caustic

Publisher‘s note. We regret that the published version of this article erroneously denoted the first author as corresponding author; in fact the formal corresponding author of this paper is Professor Taeho Lee, whose address is repeated below.

scholarly journals A Head Formulation for the Steady-State Analysis of Water Distribution Systems Using an Explicit and Exact Expression of the Colebrook–White Equation

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1163
Author(s):  
Mengning Qiu ◽  
Avi Ostfeld
Keyword(s):  
Steady State ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Solution Method ◽  
Water Distribution System ◽  
Academic Research ◽  
Exact Expression ◽  
Contamination Source

Steady-state demand-driven water distribution system (WDS) solution is the bedrock for much research conducted in the field related to WDSs. WDSs are modeled using the Darcy–Weisbach equation with the Swamee–Jain equation. However, the Swamee–Jain equation approximates the Colebrook–White equation, errors of which are within 1% for ϵ/D∈[10−6,10−2] and Re∈[5000,108]. A formulation is presented for the solution of WDSs using the Colebrook–White equation. The correctness and efficacy of the head formulation have been demonstrated by applying it to six WDSs with the number of pipes ranges from 454 to 157,044 and the number of nodes ranges from 443 to 150,630. The addition of a physically and fundamentally more accurate WDS solution method can improve the quality of the results achieved in both academic research and industrial application, such as contamination source identification, water hammer analysis, WDS network calibration, sensor placement, and least-cost design and operation of WDSs.

scholarly journals Strategies for Improving Optimal Positioning of Quality Sensors in Urban Drainage Systems for Non-Conservative Contaminants

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 934
Author(s):  
Mariacrocetta Sambito ◽  
Gabriele Freni
Keyword(s):  
Treatment Plant ◽  
Optimal Solution ◽  
Monitoring Network ◽  
Computational Effort ◽  
Bayesian Optimization ◽  
Urban Drainage ◽  
Optimization Approach ◽  
Sensor Location ◽  
Contamination Source ◽  
Monitoring Point

In the urban drainage sector, the problem of polluting discharges in sewers may act on the proper functioning of the sewer system, on the wastewater treatment plant reliability and on the receiving water body preservation. Therefore, the implementation of a chemical monitoring network is necessary to promptly detect and contain the event of contamination. Sensor location is usually an optimization exercise that is based on probabilistic or black-box methods and their efficiency is usually dependent on the initial assumption made on possible eligibility of nodes to become a monitoring point. It is a common practice to establish an initial non-informative assumption by considering all network nodes to have equal possibilities to allocate a sensor. In the present study, such a common approach is compared with different initial strategies to pre-screen eligible nodes as a function of topological and hydraulic information, and non-formal 'grey' information on the most probable locations of the contamination source. Such strategies were previously compared for conservative xenobiotic contaminations and now they are compared for a more difficult identification exercise: the detection of nonconservative immanent contaminants. The strategies are applied to a Bayesian optimization approach that demonstrated to be efficient in contamination source location. The case study is the literature network of the Storm Water Management Model (SWMM) manual, Example 8. The results show that the pre-screening and ‘grey’ information are able to reduce the computational effort needed to obtain the optimal solution or, with equal computational effort, to improve location efficiency. The nature of the contamination is highly relevant, affecting monitoring efficiency, sensor location and computational efforts to reach optimality.

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