Nitrification of high strength ammonia wastewtaer treatment - process selection is the major factor.

Biological nitrogen removal via the nitrite pathway in wastewater treatment is very important in Saving the cost of aeration and as an electron donor for denitrification. Wastewater nitrification and nitrite accumulation were carried out in a biofilm airlift reactor with autotrophic nitrifying biofilm. The biofilm reactor showed almost complete nitrification and most of the oxidized ammonium was present as nitrite at the ammonium load of 1.5 to 3.5 kg N/m3.d. Nitrite accumulation was stably achieved by the selective inhibition of nitrite oxidizers with free ammonia and dissolved oxygen limitation. Stable 100% conversion to nitrite could also be achieved even under the absence of free ammonia inhibition on nitrite oxidizers. Batch ammonium oxidation and nitrite oxidation with nitrite accumulating nitrifying biofilm showed that nitrite Oxidation was completely inhibited when free ammonia is higher than 0.2 mg N/L. However, nitrite oxidation activity was recovered as soon as the free ammonia concentration was below the threshold level when dissolved oxygen concentration was not the limiting factor. Fluorescence in situ hybridization analysis of cryosectioned nitrite accumulating nitrifying biofilm showed that the β-subclass of Proteobacteria, where ammonia oxidizers belong, was distributed outside the biofilm whereas the α-subclass of Proteobacteria, where nitrite oxidizers belong, was found mainly in the inner part of the biofilm. It is likely that dissolved oxygen deficiency or limitation in the inner part of the nitrifying biofilm, where nitrite oxidizers exist, is responsible for the complete shut down of the nitrite oxidizers activity under the absence of free ammonia inhibition.

A quality and cost approach for welding process selection

The aim of this work was to propose, apply and evaluate a methodical approach to select welding processes in a productive environment based on market requirements of Quality and Costs. A case study was used. The welds were carried out in laboratory, simulating the joint conditions of a manufacturer and using several welding processes: SMAW, GTAW, pulsed GTAW, GMAW with CO2 and Ar based shielding gases and pulsed GMAW. For Quality analysis geometrical aspects of the beads were considered and for Cost analysis, welding parameters and consumable prices. Quantitative indices were proposed and evaluated. After that, evaluation of both Quality and Costs was done, showing to be possible to select the most suitable welding process to a specific application, taking into account the market conditions of a company.

Knowledge-based system for casting process selection

Design of casting entails the knowledge of various interacting factors that are unique to casting process, and, quite often, product designers do not have the required foundry-specific knowledge. Casting designers normally have to liaise with casting experts in order to ensure the product designed is castable and the optimum casting method is selected. This two-way communication results in long design lead times, and lack of it can easily lead to incorrect casting design. A computer-based system at the discretion of a design engineer can, however, alleviate this problem and enhance the prospect of casting design for manufacture. This paper proposes a knowledge-based expert system approach to assist casting product designers in selecting the most suitable casting process for specified casting design requirements, during the design phase of product manufacture. A prototype expert system has been developed, based on production rules knowledge representation technique. The proposed system consists of a number of autonomous but interconnected levels, each dealing with a specific group of factors, namely, casting alloy, shape and complexity parameters, accuracy requirements and comparative costs, based on production quantity. The user interface has been so designed to allow the user to have a clear view of how casting design parameters affect the selection of various casting processes at each level; if necessary, the appropriate design changes can be made to facilitate the castability of the product being designed, or to suit the design to a preferred casting method.

Process Selection Using Variation and Cost Relations

Variations are inherent in all manufacturing processes and can significantly affect the quality of a final assembly, particularly in multistage assembly systems. Existing research in variation management has primarily focused on incorporating GD&T factors into variation propagation models in order to predict product quality and allocate tolerances. However, process induced variation, which has a key influence on process planning, has not been fully studied. Furthermore, the link between variation and cost has not been well established, in particular the effect that assembly process selection has on the final quality and cost of a product. To overcome these barriers, this paper proposes a novel method utilizing process capabilities to establish the relationship between variation and cost. The methodology is discussed using a real industrial case study. The benefits include determining the optimum configuration of an assembly system and facilitating rapid introduction of novel assembly techniques to achieve a competitive edge.

Designing next generation car washing machines with rotary brushes

Tese de Doutoramento - Leaders for Technical Industries (LTI) - MIT Portugal

Integrating life cycle costs and environmental impacts of composite rail car-bodies for a Korean train

Background, aim, and scope A coupled Life Cycle Costing and life cycle assessment has been performed for car-bodies of the Korean Tilting Train eXpress (TTX) project using European and Korean databases, with the objective of assessing environmental and cost performance to aid materials and process selection. More specifically, the potential of polymer composite car-body structures for the Korean Tilting Train eXpress (TTX) has been investigated. Materials and methods This assessment includes the cost of both carriage manufacturing and use phases, coupled with the life cycle environmental impacts of all stages from raw material production, through carriage manufacture and use, to end-of-life scenarios. Metallic carriages were compared with two composite options: hybrid steel-composite and full-composite carriages. The total planned production for this regional Korean train was 440 cars, with an annual production volume of 80 cars. Results and discussion The coupled analyses were used to generate plots of cost versus energy consumption and environmental impacts. The results show that the raw material and manufacturing phase costs are approximately half of the total life cycle costs, whilst their environmental impact is relatively insignificant (3-8%). The use phase of the car-body has the largest environmental impact for all scenarios, with near negligible contributions from the other phases. Since steel rail carriages weigh more (27-51%), the use phase cost is correspondingly higher, resulting in both the greatest environmental impact and the highest life cycle cost. Compared to the steel scenario, the hybrid composite variant has a lower life cycle cost (16%) and a lower environmental impact (26%). Though the full composite rail carriage may have the highest manufacturing cost, it results in the lowest total life cycle costs and lowest environmental impacts. Conclusions and recommendations This coupled cost and life cycle assessment showed that the full composite variant was the optimum solution. This case study showed that coupling of technical cost models with life cycle assessment offers an efficient route to accurately evaluate economic and environmental performance in a consistent way.

On-line –jatkojalostuksen integrointi mäntysahan tuotantoprosessiin

Diplomityön tavoitteena oli selvittää erilaisia jatkojalostusmahdollisuuksia, joilla voidaan nostaa suuren mäntysahan tuotteiston arvoa. Lisäksi tuli tarkastella jalostuksen integrointia sahan tuotantoprosessiin. Työn taustalla on toisaalta puutuotemarkkinoiden muuttuminen ja toisaalta raaka-aineen laadullinen huononeminen. Molemmat seikat vaikuttavat negatiivisesti perinteisen mäntysahan kannattavuuteen .Jatkojalostuksen integroinnilla sahatavaraprosessiin saavutetaan säästöjä tuotantokustannuksissa, kun tarkastellaan koko prosessia tukista jatkojalosteeksi. Myös raaka-aineen tuottavuutta voidaan nostaa integraation avulla. Jatkojalostus voidaan integroida sahatavaraprosessiin raaka-aineen valikoinnilla sahatavaraprosessin eri osissa, on-line –jalostuksella sekä taloudellisesti. Sahatavaraprosessissa tapahtuva raaka-aineen valikointi voidaan suorittaa tukeista ja sahatavarasta. Valikointikriteerinä voi olla puun ominaisuudet, sahatavaran mitat ja laatu. Valikointiin voidaan nykyteknologiasta hyödyntää röntgentekniikkaa sekä konenäköä. On-line –jalostus tarkoittaa kiinteästi sahatavaraprosessiin liittyvää jalostusta, jolloin ns. turhia prosessivaiheita jää pois ja syntyy säästöjä. On-line –jalostuksen edellytys on raaka-aineen jonkin asteinen valikointi, esim. pituus. Taloudellisesti integroitu jalostus tarkoittaa, että jalostuslaitoksella pyritään nollatulokseen ja jalostuksen lisäarvo palautetaan sahan toimittamaan raaka-aineen hintaan. Tällainen toiminta yhtiön sisällä poistaa turhaa keskustelua raaka-aineen siirtohinnoista ja siten vapauttaa osaltaan resursseja tuottavampaan toimintaan. Erilaisten jatkojalostusmuotojen ja puun ominaisuuksien hyödyntämisen seulonnan perusteella löytyi yksi jalostusmuoto, jolla voidaan kohottaa mäntysahan tuotteiston arvoa. Työn tuloksena syntyi investointiehdotus aihiotankotuotannosta ikkunateollisuuden tarpeisiin. Raaka-aineen hyödynnettäviä ominaisuuksia ovat männyn sydänpuun luonnollinen kestävyys sekä keskimääräinen oksaväli. Valikointi tehdään välitukeista, joiden sahaamisen kannattavuus on männyn rungon osista heikoin. Aihiotankoprosessissa hyödynnetään konenäköä ja sormijatkostekniikkaa. Jatkojalostuksen integrointi sahatavaraprosessiin toteutetaan rakentamalla on-line –jalostuslaitos sekä soveltamalla röntgentekniikkaa raaka-aineen valinnassa.

Otimização multiobjetivo no planejamento agregado da produção e na cogeração de energia elétrica de usina do setor sucroenergético

Pós-graduação em Engenharia Mecânica - FEG

Extracción de conocimientos de la diabetes tipo 1 utilizando la metodología de "Data Mining"

La diabetes mellitus es un trastorno en la metabolización de los carbohidratos, caracterizado por la nula o insuficiente segregación de insulina (hormona producida por el páncreas), como resultado del mal funcionamiento de la parte endocrina del páncreas, o de una creciente resistencia del organismo a esta hormona. Esto implica, que tras el proceso digestivo, los alimentos que ingerimos se transforman en otros compuestos químicos más pequeños mediante los tejidos exocrinos. La ausencia o poca efectividad de esta hormona polipéptida, no permite metabolizar los carbohidratos ingeridos provocando dos consecuencias: Aumento de la concentración de glucosa en sangre, ya que las células no pueden metabolizarla; consumo de ácidos grasos mediante el hígado, liberando cuerpos cetónicos para aportar la energía a las células. Esta situación expone al enfermo crónico, a una concentración de glucosa en sangre muy elevada, denominado hiperglucemia, la cual puede producir a medio o largo múltiples problemas médicos: oftalmológicos, renales, cardiovasculares, cerebrovasculares, neurológicos… La diabetes representa un gran problema de salud pública y es la enfermedad más común en los países desarrollados por varios factores como la obesidad, la vida sedentaria, que facilitan la aparición de esta enfermedad. Mediante el presente proyecto trabajaremos con los datos de experimentación clínica de pacientes con diabetes de tipo 1, enfermedad autoinmune en la que son destruidas las células beta del páncreas (productoras de insulina) resultando necesaria la administración de insulina exógena. Dicho esto, el paciente con diabetes tipo 1 deberá seguir un tratamiento con insulina administrada por la vía subcutánea, adaptado a sus necesidades metabólicas y a sus hábitos de vida. Para abordar esta situación de regulación del control metabólico del enfermo, mediante una terapia de insulina, no serviremos del proyecto “Páncreas Endocrino Artificial” (PEA), el cual consta de una bomba de infusión de insulina, un sensor continuo de glucosa, y un algoritmo de control en lazo cerrado. El objetivo principal del PEA es aportar al paciente precisión, eficacia y seguridad en cuanto a la normalización del control glucémico y reducción del riesgo de hipoglucemias. El PEA se instala mediante vía subcutánea, por lo que, el retardo introducido por la acción de la insulina, el retardo de la medida de glucosa, así como los errores introducidos por los sensores continuos de glucosa cuando, se descalibran dificultando el empleo de un algoritmo de control. Llegados a este punto debemos modelar la glucosa del paciente mediante sistemas predictivos. Un modelo, es todo aquel elemento que nos permita predecir el comportamiento de un sistema mediante la introducción de variables de entrada. De este modo lo que conseguimos, es una predicción de los estados futuros en los que se puede encontrar la glucosa del paciente, sirviéndonos de variables de entrada de insulina, ingesta y glucosa ya conocidas, por ser las sucedidas con anterioridad en el tiempo. Cuando empleamos el predictor de glucosa, utilizando parámetros obtenidos en tiempo real, el controlador es capaz de indicar el nivel futuro de la glucosa para la toma de decisones del controlador CL. Los predictores que se están empleando actualmente en el PEA no están funcionando correctamente por la cantidad de información y variables que debe de manejar. Data Mining, también referenciado como Descubrimiento del Conocimiento en Bases de Datos (Knowledge Discovery in Databases o KDD), ha sido definida como el proceso de extracción no trivial de información implícita, previamente desconocida y potencialmente útil. Todo ello, sirviéndonos las siguientes fases del proceso de extracción del conocimiento: selección de datos, pre-procesado, transformación, minería de datos, interpretación de los resultados, evaluación y obtención del conocimiento. Con todo este proceso buscamos generar un único modelo insulina glucosa que se ajuste de forma individual a cada paciente y sea capaz, al mismo tiempo, de predecir los estados futuros glucosa con cálculos en tiempo real, a través de unos parámetros introducidos. Este trabajo busca extraer la información contenida en una base de datos de pacientes diabéticos tipo 1 obtenidos a partir de la experimentación clínica. Para ello emplearemos técnicas de Data Mining. Para la consecución del objetivo implícito a este proyecto hemos procedido a implementar una interfaz gráfica que nos guía a través del proceso del KDD (con información gráfica y estadística) de cada punto del proceso. En lo que respecta a la parte de la minería de datos, nos hemos servido de la denominada herramienta de WEKA, en la que a través de Java controlamos todas sus funciones, para implementarlas por medio del programa creado. Otorgando finalmente, una mayor potencialidad al proyecto con la posibilidad de implementar el servicio de los dispositivos Android por la potencial capacidad de portar el código. Mediante estos dispositivos y lo expuesto en el proyecto se podrían implementar o incluso crear nuevas aplicaciones novedosas y muy útiles para este campo. Como conclusión del proyecto, y tras un exhaustivo análisis de los resultados obtenidos, podemos apreciar como logramos obtener el modelo insulina-glucosa de cada paciente. ABSTRACT. The diabetes mellitus is a metabolic disorder, characterized by the low or none insulin production (a hormone produced by the pancreas), as a result of the malfunctioning of the endocrine pancreas part or by an increasing resistance of the organism to this hormone. This implies that, after the digestive process, the food we consume is transformed into smaller chemical compounds, through the exocrine tissues. The absence or limited effectiveness of this polypeptide hormone, does not allow to metabolize the ingested carbohydrates provoking two consequences: Increase of the glucose concentration in blood, as the cells are unable to metabolize it; fatty acid intake through the liver, releasing ketone bodies to provide energy to the cells. This situation exposes the chronic patient to high blood glucose levels, named hyperglycemia, which may cause in the medium or long term multiple medical problems: ophthalmological, renal, cardiovascular, cerebrum-vascular, neurological … The diabetes represents a great public health problem and is the most common disease in the developed countries, by several factors such as the obesity or sedentary life, which facilitate the appearance of this disease. Through this project we will work with clinical experimentation data of patients with diabetes of type 1, autoimmune disease in which beta cells of the pancreas (producers of insulin) are destroyed resulting necessary the exogenous insulin administration. That said, the patient with diabetes type 1 will have to follow a treatment with insulin, administered by the subcutaneous route, adapted to his metabolic needs and to his life habits. To deal with this situation of metabolic control regulation of the patient, through an insulin therapy, we shall be using the “Endocrine Artificial Pancreas " (PEA), which consists of a bomb of insulin infusion, a constant glucose sensor, and a control algorithm in closed bow. The principal aim of the PEA is providing the patient precision, efficiency and safety regarding the normalization of the glycemic control and hypoglycemia risk reduction". The PEA establishes through subcutaneous route, consequently, the delay introduced by the insulin action, the delay of the glucose measure, as well as the mistakes introduced by the constant glucose sensors when, decalibrate, impede the employment of an algorithm of control. At this stage we must shape the patient glucose levels through predictive systems. A model is all that element or set of elements which will allow us to predict the behavior of a system by introducing input variables. Thus what we obtain, is a prediction of the future stages in which it is possible to find the patient glucose level, being served of input insulin, ingestion and glucose variables already known, for being the ones happened previously in the time. When we use the glucose predictor, using obtained real time parameters, the controller is capable of indicating the future level of the glucose for the decision capture CL controller. The predictors that are being used nowadays in the PEA are not working correctly for the amount of information and variables that it need to handle. Data Mining, also indexed as Knowledge Discovery in Databases or KDD, has been defined as the not trivial extraction process of implicit information, previously unknown and potentially useful. All this, using the following phases of the knowledge extraction process: selection of information, pre- processing, transformation, data mining, results interpretation, evaluation and knowledge acquisition. With all this process we seek to generate the unique insulin glucose model that adjusts individually and in a personalized way for each patient form and being capable, at the same time, of predicting the future conditions with real time calculations, across few input parameters. This project of end of grade seeks to extract the information contained in a database of type 1 diabetics patients, obtained from clinical experimentation. For it, we will use technologies of Data Mining. For the attainment of the aim implicit to this project we have proceeded to implement a graphical interface that will guide us across the process of the KDD (with graphical and statistical information) of every point of the process. Regarding the data mining part, we have been served by a tool called WEKA's tool called, in which across Java, we control all of its functions to implement them by means of the created program. Finally granting a higher potential to the project with the possibility of implementing the service for Android devices, porting the code. Through these devices and what has been exposed in the project they might help or even create new and very useful applications for this field. As a conclusion of the project, and after an exhaustive analysis of the obtained results, we can show how we achieve to obtain the insulin–glucose model for each patient.

Prediction of protein folding class using global description of amino acid sequence.

We present a method for predicting protein folding class based on global protein chain description and a voting process. Selection of the best descriptors was achieved by a computer-simulated neural network trained on a data base consisting of 83 folding classes. Protein-chain descriptors include overall composition, transition, and distribution of amino acid attributes, such as relative hydrophobicity, predicted secondary structure, and predicted solvent exposure. Cross-validation testing was performed on 15 of the largest classes. The test shows that proteins were assigned to the correct class (correct positive prediction) with an average accuracy of 71.7%, whereas the inverse prediction of proteins as not belonging to a particular class (correct negative prediction) was 90-95% accurate. When tested on 254 structures used in this study, the top two predictions contained the correct class in 91% of the cases.

The Performance Characteristics of a Surface-Modified Cutting Tool

In the past, many papers have been presented which show that the coating of cutting tools often yields decreased wear rates and reduced coefficients of friction. Although different theories are proposed, covering areas such as hardness theory, diffusion barrier theory, thermal barrier theory, and reduced friction theory, most have not dealt with the question of how and why the coating of tool substrates with hard materials such as Titanium Nitride (TiN), Titanium Carbide (TiC) and Aluminium Oxide (Al203) transforms the performance and life of cutting tools. This project discusses the complex interrelationship that encompasses the thermal barrier function and the relatively low sliding friction coefficient of TiN on an undulating tool surface, and presents the result of an investigation into the cutting characteristics and performance of EDMed surface-modified carbide cutting tool inserts. The tool inserts were coated with TiN by the physical vapour deposition (PVD) method. PVD coating is also known as Ion-plating which is the general term of the coating method in which the film is created by attracting ionized metal vapour in this the metal was Titanium and ionized gas onto negatively biased substrate surface. Coating by PVD was chosen because it is done at a temperature of not more than 5000C whereas chemical Vapour Deposition CVD process is done at very high temperature of about 8500C and in two stages of heating up the substrates. The high temperatures involved in CVD affects the strength of the (tool) substrates. In this study, comparative cutting tests using TiN-coated control specimens with no EDM surface structures and TiN-coated EDMed tools with a crater-like surface topography were carried out on mild steel grade EN-3. Various cutting speeds were investigated, up to an increase of 40% of the tool manufacturer’s recommended speed. Fifteen minutes of cutting were carried out for each insert at the speeds investigated. Conventional tool inserts normally have a tool life of approximately 15 minutes of cutting. After every five cuts (passes) microscopic pictures of the tool wear profiles were taken, in order to monitor the progressive wear on the rake face and on the flank of the insert. The power load was monitored for each cut taken using an on-board meter on the CNC machine to establish the amount of power needed for each stage of operation. The spindle drive for the machine is an 11 KW/hr motor. Results obtained confirmed the advantages of cutting at all speeds investigated using EDMed coated inserts, in terms of reduced tool wear and low power loads. Moreover, the surface finish on the workpiece was consistently better for the EDMed inserts. The thesis discusses the relevance of the finite element method in the analysis of metal cutting processes, so that metal machinists can design, manufacture and deliver goods (tools) to the market quickly and on time without going through the hassle of trial and error approach for new products. Improvements in manufacturing technologies require better knowledge of modelling metal cutting processes. Technically the use of computational models has a great value in reducing or even eliminating the number of experiments traditionally used for tool design, process selection, machinability evaluation, and chip breakage investigations. In this work, much interest in theoretical and experimental investigations of metal machining were given special attention. Finite element analysis (FEA) was given priority in this study to predict tool wear and coating deformations during machining. Particular attention was devoted to the complicated mechanisms usually associated with metal cutting, such as interfacial friction; heat generated due to friction and severe strain in the cutting region, and high strain rates. It is therefore concluded that Roughened contact surface comprising of peaks and valleys coated with hard materials (TiN) provide wear-resisting properties as the coatings get entrapped in the valleys and help reduce friction at chip-tool interface. The contributions to knowledge: a. Relates to a wear-resisting surface structure for application in contact surfaces and structures in metal cutting and forming tools with ability to give wear-resisting surface profile. b. Provide technique for designing tool with roughened surface comprising of peaks and valleys covered in conformal coating with a material such as TiN, TiC etc which is wear-resisting structure with surface roughness profile compose of valleys which entrap residual coating material during wear thereby enabling the entrapped coating material to give improved wear resistance. c. Provide knowledge for increased tool life through wear resistance, hardness and chemical stability at high temperatures because of reduced friction at the tool-chip and work-tool interfaces due to tool coating, which leads to reduced heat generation at the cutting zones. d. Establishes that Undulating surface topographies on cutting tips tend to hold coating materials longer in the valleys, thus giving enhanced protection to the tool and the tool can cut faster by 40% and last 60% longer than conventional tools on the markets today.

Group selection and kin selection: two concepts but one process.

In a recent paper, Traulsen and Nowak use a multilevel selection model to show that cooperation can be favored by group selection in finite populations [Traulsen A, Nowak M (2006) Proc Natl Acad Sci USA 103:10952-10955]. The authors challenge the view that kin selection may be an appropriate interpretation of their results and state that group selection is a distinctive process "that permeates evolutionary processes from the emergence of the first cells to eusociality and the economics of nations." In this paper, we start by addressing Traulsen and Nowak's challenge and demonstrate that all their results can be obtained by an application of kin selection theory. We then extend Traulsen and Nowak's model to life history conditions that have been previously studied. This allows us to highlight the differences and similarities between Traulsen and Nowak's model and typical kin selection models and also to broaden the scope of their results. Our retrospective analyses of Traulsen and Nowak's model illustrate that it is possible to convert group selection models to kin selection models without disturbing the mathematics describing the net effect of selection on cooperation.

A survey on the concepts used in the hearing aid selection process for hearing-impaired adults

This paper is a review of current practices in 1976 among audiologists and hearing aid dealers in fitting of hearing aids for adults.

The road not taken: observing a process of issue selection and model conceptualization

This article describes a case study involving information technology managers and their new programmer recruitment policy, but the primary interest is methodological. The processes of issue generation and selection and model conceptualization are described. Early use of “magnetic hexagons” allowed the generation of a range of issues, most of which would not have emerged if system dynamics elicitation techniques had been employed. With the selection of a specific issue, flow diagraming was used to conceptualize a model, computer implementation and scenario generation following naturally. Observations are made on the processes of system dynamics modeling, particularly on the need to employ general techniques of knowledge elicitation in the early stages of interventions. It is proposed that flexible approaches should be used to generate, select, and study the issues, since these reduce any biasing of the elicitation toward system dynamics problems and also allow the participants to take up the most appropriate problem- structuring approach.