Tag Archives: Process Control

[IT] Risparmio Energetico e Miglioramento Depurativo (C,N,P) mediante Controllo a SetPoint Dinamico dell’Ossigeno Disciolto: WDOxy Fuzzy

Lc2WDOxy-Fuzzy Controller

– Concentrazione dell’Ossigeno Disciolto e Controllo Energetico: un controllo adeguato del funzionamento di un impianto di depurazione e in particolare, del reattore biologico  (CSTR a “fanghi attivi” e con rimozione di N e P), si rende necessario sia per garantire la qualità dell’effluente e il rispetto dei limiti di legge, sia per contenere le spese di gestione: aspetto quest’ultimo che sta assumendo un’importanza sempre maggiore a causa dei crescenti costi dell’energia.  Infatti, il controllo della fornitura di aria in un impianto a fanghi attivi è importante per le seguenti motivazioni:

  • la fornitura di ossigeno è una delle principali voci di costo gestionali (10÷30 %);
  • la fornitura di ossigeno è un fattore determinante per l’affidabilità della qualità dell’effluente depurato;
  • la fornitura di ossigeno è un fattore determinante per l’efficienza della sedimentazione dei fanghi e dello stato di salute della biomassa.

Concentrazione dell’Ossigeno Disciolto e Bulking Filamentoso: la concentrazione dell’ossigeno disciolto (OD) nel reattore è un parametro di input di enorme importanza per la sua influenza sul bulking filamentoso e quindi, sulla sedimentabilità dei fanghi. La relazione tra il OD e lo SVI è direttamente influenzata dal carico organico (F/M): più elevato è il carico organico, più alta è la concentrazione di ossigeno disciolto necessaria per prevenire il bulking. La proliferazione di alcuni batteri filamentosi quali lo S.Natans, tipo 1701, e l’H. hydrossis in condizioni di basso ossigeno disciolto può essere attribuita all’elevata affinità (bassa costante di semisaturazione) che essi hanno per l’ossigeno.

Il controllo tradizionale con  Set-Point Prefissato dell’ossigeno disciolto:

  • Non si tiene conto della resa del processo di depurazione: necessaria la misura di un altro parametro (efficienza abbattimento NH4)
  • Non si tiene conto della variabilità del carico entrante: si fornisce troppo o troppo poco ossigeno per la maggior parte del tempo
  • Scarsa stabilità di controllo: i metodi di controllo tradizionali sono troppo semplificati e danno luogo ad instabilità

WDOxy

Il Modello WDOxy Fuzzy è una procedura dinamica di calcolo del Set-Point dell’OD, ovvero della concentrazione di ossigeno disciolto (minima) necessaria per le effettive esigenze real-time del metabolismo batterico della rimozione del carbonio e dell’azoto. La procedura WDOxy Fuzzy si basa su algoritmi bio-processistici e sull’utilizzo in “input” della misura on-line del valore di concentrazione NH4 (in alternativa: ORP), oltre alla misura on-line dell’OD e restituisce in “output” in tempo reale, il valore di set-point ottimale di OD.

Vantaggi del sistema a Set-Point OD Dinamico WDOxy-Fuzzy rispetto al sistema tradizionale a Set-Point Prefissato: 

  • Risposta immediata a picchi entranti e condizioni di variabilità di carico entrante grazie ad un adattamento continuo del set-point di ossigeno disciolto: adattamento del processo biologico alle variazioni di carico in ingresso. Il sistema a set-point OD Dinamico, a differenza del sistema di controllo tradizionale (a set – point fisso di ossigeno disciolto) che evidenzia ampie oscillazioni, dimostra una notevole stabilità nel raggiungimento delle condizioni di processo ottimali, anche di fronte a significative variazioni del carico entrante
  • Maggiore stabilità di processo ed efficienza depurativa, con particolare riferimento al processo di nitrificazione;
  • Elevato risparmio energetico 15-20%  e contestuale eliminazione degli eccessi di nitrificazione, in quanto viene evitata la fornitura di aria in eccesso ed ottenendo un miglior rendimento di trasferimento di ossigeno da parte dei diffusori.
  • Assicura l’efficienza del rendimento di rimozione richiesto.

SOLUZIONE a Set-Point Dinamico:  Liquicontrol NDP

LcLiquidcontrol NDP

EH

Liquicontrol NDP è un innovativo sistema di gestione e controllo dell’ossigeno disciolto in vasca d’aerazione e della concentrazione dell’azoto ammoniacale nell’effluente. Il valore di Azoto ammoniacale viene misurato in continuo, confrontato in tempo reale con il valore desiderato ed infine, utilizzato per il calcolo del set-point variabile dell’ossigeno disciolto. Il valore del set-point di ossigeno disciolto è poi confrontato con la misura dell’ossigeno disciolto presente in vasca in quel momento e determina, grazie ad una regolazione con logica fuzzy, l’erogazione dell’aria.

Per la stima del risparmio energetico relativo al sistema Liquicontrol NDP, è possibile utilizzare un parametro denominato Indice di Prestazione ENergetica: rapporto tra l’energia attiva assorbita dal comparto di aerazione ed i più significativi carichi inquinanti rimossi, pesati secondo l’effettivo contributo alla fornitura d’aria:

IPEN = Energia (kWh/d) / [0,3*CODrimosso (kg/d)+0,7*NH4+rimosso(kg/d)]

SISI-EHLiquicontrol

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Knowledge Management vs Knowledge Engineering

 Knowledge Management vs Knowledge Engineering

The terms knowledge management and knowledge engineering seem to be used as interchangeably as the terms data and information used to be. But if you were to ask either a manager or an engineer if their jobs were the same, I doubt if you would get them to agree they were. A brief examination of the terms management and engineering shows that to manage is to exercise executive, administrative and supervisory direction, where as, to engineer is to lay out, construct, or contrive or plan out, usually with more or less subtle skill and craft.

The main difference seems to be that the (knowledge) manager establishes the direction the process should take, where as the (knowledge) engineer develops the means to accomplish that direction. Not all that much different from the relationships in any other discipline. So therefor we should find the knowledge managers concerned with the knowledge needs of the enterprise. We should see them doing the research to understand what knowledge is needed to make what decision and enable what actions. They should be taking a key role in the design of the enterprise and from the needs of the enterprise establishing the enterprise level knowledge management policies. It is to the knowledge managers that the user should go with their “need to know“.

On the other hand, if we were look in on the knowledge engineers we should find them working on such areas as data and information representation and encoding methodologies, data repositories, work flow management, groupware technologies, etc,. The knowledge engineers would most likely be researching the technologies needed to meet the enterprise’s knowledge management needs. The knowledge engineers should also be establishing the processes by which knowledge requests are examined, information assembled, and knowledge returned to the requestor. What is significant in both of these “job descriptions” is that nowhere do I claim that either is the “owner” of the enterprise knowledge, information, or data. Ownership remains the prerogative of the enterprise, or the enterprise element manager, or even the individual depending on the established policies for enterprise level knowledge ownership. As we might well expect, other views exist as to the roles of the knowledge manager and the knowledge engineer. For example, to the developer of knowledge-base computer software systems, the knowledge engineer is most likely a computer scientist specializing the development of artificial intelligence knowledge bases. From the view of the corporate board-room the knowledge manager may be the Chief Information Officer (CIO) or the person in charge of the Information Resource Management (IRM). The point is, when discussing terms such as knowledge manager or knowledge engineer, or any other role designation, it is important that all parties share a clear mutual understanding.  Brian D. Newman © January 5, 1996

Multi-Perspective Modeling for Knowledge Management  and Knowledge Engineering (John Kingston)  Multi-Perspective Modeling

The purpose of this thesis is to show how an analytical framework originally intended for information systems architecture can be used to support knowledge management, knowledge engineering and the closely related discipline of ontology engineering. The framework suggests analysing information or knowledge from six perspectives (Who,What, How, When, Where and Why) at up to six levels of detail (ranging from “scoping” the problem to an implemented solution). The application of this framework to each of CommonKADS’ models is discussed, in the context of several practical applications of the CommonKADS methodology. Strengths and weaknesses in the models that are highlighted by the practical applications are analysed using the framework,
with the overall goal of showing where CommonKADS is currently useful and where it could be usefully extended. The same framework is also applied to knowledge management; it is established that “knowledge management” is in fact a wide collection of different approaches and techniques, and the framework can support and extend every approach to some extent, as well as the decision which approach is best for a particular case. Specific applications of using the framework to model medical knowledge and to resolve common problems in ontology development are presented.
The thesis also includes research on mapping knowledge acquisition techniques to CommonKADS’ models (and to the framework); proposing some extensions to CommonKADS’ library of generic inference structures; and it concludes with a suggestion for a “pragmatic” KADS for use on small projects. The aim is to show that this framework both characterises the knowledge required for both knowledge management and knowledge engineering, and can provide a guide to good selection of knowledge management techniques. If the chosen technique should involve knowledge engineering, the wealth of practical advice on CommonKADS in this thesis should also be beneficial.

Knowledge Engineering

WaterEnergyFood by ANOVA

logo_sfondo_trasparente_effetto_scolpito  www.anovastudi.com

The company ANOVA Studies & Interdisciplinary Research is a private laboratory dealing in knowledge engineering and development of innovation in the context of Data IntelligenceKnowledge Modeling and real-time Process Control, with particular reference to the water industry and to energy sustainability and agribusiness.

AnovaWeb

ANOVA Studies & Interdisciplinary Research  is a small Italian company dealing with Business Development and Process Control, mainly in market of Water Industry, Energy Efficiency and Food Processing. Since 1997 located in Naples, ANOVA was born as spin-off of SESPIM R&D Consortium (1990, ALENIA – Italimpianti). After having operated in Knowledge Engineering & ICT applications for more than 15 years as a R&D Laboratory (complied to National Minister of Research Register), in the actual market scenario, ANOVA is now focused on value and on innovation development: its main strength is  an Interdisciplinary Approach  and a Knowledge Modeling Ability toward problem solving in business service. ANOVA has carried out more than 30 R&D Projects for SMEs in National (MIUR, MISE, PON, etc..) and European (LIFE CRAFT, etc.) context; more than 35 Advanced Process Control Systems (SCADA, DSS, Expert Systems), in the field of water management, environment and energy; tens of Engineering Projects in hydraulics, environmental, and about the ‘energy efficiency.  http://www.youtube.com/watch?v=0JwxurlBzC0
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Giovanni Mappa (Owner)

 GMnt Giovanni Mappa Knowledge Systems Engineer

Engineer independent researcher, working in the context of real-world applications of Knowledge Engineering. Specializing in the creation of the Interdisciplinary Models of Knowledge, aiming at developing tools and performance solutions in the management of processes and the simplification of complexity.

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“A good idea comes from our genius or by fortune,

but its value comes from the process of knowledge…

which is able to change it into a competitive benefit”  (G.M.)

[EN] WDOxy/Fuzzy-control for improved Nitrogen Removal and Energy Saving in WWTP with predenitrification

WDOxydev

Application of fuzzy-logic to improve the control of the activated sludge process. WDOxy-Controller is a dynamic (non-deterministic/fuzzy) approach model to “Dissolved Oxygen Control Strategy” in wastewater nutrient removing, basically based on using of Ione Selective (NH4+, NO3-) on-line analyzers, but in option it is possible to use on-line ORP/pH (Redox potential) low-cost sensors too. Fuzzy-logic based control strategies for wastewater treatment plants with pre-denitrification should lead to better effluent quality and, in parallel, to a reduction of energy consumption. The WDOxy goal is to develop a “dynamic DO set-up on need” calculation able to provide a continuous nutrient removal process control, under legal outlet limits and within a sustainable energy saving control. Compared to the operation with fixed nitrification/denitrification zones and constant DO concentrations, the required air-flow could be reduced up to 24% by using fuzzy-logic based control strategies. In fact, Traditional “DO Closed Loop Control” (aeration control) is a well-known automation procedure that does not allows to timely face the real need of oxidation that biological process dynamically requires from time to time. On the contrary, WDOxy control is able to face DO requirement, in all nutrient removal biological processes (N and P), in a cost efficient way, that means, by dosing energy on the base of a real-time evaluation of biomass metabolism.

WDOxy

The WDOxy fuzzy-controller procedure can be easily implemented in modern control and supervision systems and  the control characteristics can be followed and modified during operation.

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