Lean production in the precast concrete components’ industry

Proceedings IGLC-19, July 2011, Lima, PerúThis article is a case study applying Lean Thinking and Lean Production principles in a factory that produces prefabricated reinforced concrete components. The objective is to reduce waste production and increase productivity. Nine factories were analyzed, of which one was chosen for developing an implementation model. A number of changes were proposed based on Lean concepts and the study of several similar factories to improve productivity. A Black Belt Team was created to improve the execution and continuance of the lean concepts. Value Stream Mapping was also used as an aid in the identification of existing waste and improvement opportunities, to which various Lean tools were applied in order to solve the identified problems. The main conclusions of this study are that it is possible to achieve a significant improvement in the production system of prefabricated reinforced concrete components using Lean philosophy. Improvements in the reduction of lead-time, reduction of waste and increase in productivity are achieved with simple and low cost technique

Produção Lean na indústria de pré-fabricados de betão armado: aplicação e avaliação de resultados em caso de estudo

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Civil, Perfil de ConstruçãoLean é uma filosofia de produção nascida após a segunda guerra mundial no sector automóvel com intuito de optimizar o sistema de produção. O conceito desta filosofia baseia-se na eliminação de desperdício. Actualmente é aplicada em diversas áreas obtendo-se resultados positivos, tais como: redução de custos, aumento da qualidade, redução de tempo de processamento e aumento de produtividade. Esta dissertação aborda e aplica os princípios fundamentais de Lean Thinking e Lean Production, numa fábrica de elementos pré-fabricados de betão armado. Os objectivos são de reduzir os desperdícios da produção e aumentar a produtividade. Foram analisadas nove fábricas, das quais foi escolhida uma para elaborar um modelo de implementação com diversas propostas de alterações com base nos conceitos Lean e no estudo das diversas fábricas para melhorar produtividade. Neste modelo foi desenvolvido um Black Belt Team, para uma boa implementação e manutenção dos conceitos Lean. Foi também utilizado o Mapeamento do Fluxo de Valor, como auxílio na identificação dos desperdícios existentes e oportunidades de melhoria, ao qual depois foram aplicadas diversas ferramentas e metodologias Lean para solucionar os problemas identificados. As principais conclusões deste estudo são que é possível uma melhoria significativa no sistema de produção de elementos pré-fabricados em betão armado aplicando a filosofia Lean. Melhorias na redução do tempo de produção, na redução dos desperdícios e no aumento da produtividade com técnicas de implementação simples e de baixo custo de intervenção

AI-based SAR-to-optical GAI regression for crop monitoring

The green area index (GAI) is a key biophysical variable for crop monitoring. The most accurate methods for its large-scale estimation rely on optical remote sensing data. However, these can be hampered by frequent cloud cover. In this context, synthetic aperture radar (SAR) offers the advantage of being able to provide dense time series that can be used to complement the sparse GAI series derived from optical data. In this study, SAR-to-optical GAI regression is performed using a transformer neural network with past and current values of SAR backscatter and interferometric coherence, as well as past values of GAI when available. Sentine-1 and -2 data acquired from 2018 to 2021 over the Hesbaye region of Belgium are used for cross-validation. The model is trained on three growing seasons and tested on the fourth for each fold. The results show that the model can successfully predict Sentinel-2-derived GAI with an average R2=0.88 and RMSE=0.74, outperforming methods relying on radiative transfer model (e.g., Water Cloud model) inversion. The method is also validated with data collected in situ in eight maize fields in Belgium (R2=0.87 and RMSE=0.75). These promising results pave the way for the generation of accurate, dense GAI time series throughout the growing season, allowing for timely crop monitoring in cloud-prone regions

Leaf area index retrieval in Shanxi province of China using Sentinel-1 data

The green area index (GAI), i.e., half of the green leaf and stem area per unit of horizontal ground surface area, is a key variable for assessing the development, health, and productivity of crops. Currently, most large-scale and cost-effective methods for its estimation exploit optical remote sensing data. Frequent cloud cover can, however, hinder their reliability by blocking the view of the sensors on which they rest. As a result in many parts of the world, its timely monitoring cannot be ensured using optical systems alone. Synthetic aperture radars (SARs), however, thanks to their cloud-penetrating ability, are capable of producing dense time series that can be used to improve the spatial and temporal coverage of their optical counterparts. In this study, SAR-to-optical GAI regression has been performed using a transformer encoder with past and current values of SAR backscatter and interferometric coherence, as well as past values of LAI when available. Sentinel-1 and -2 images acquired from 2018 to 2021 over the Hesbaye region of Belgium have been used for cross-validation. The model has been trained on three growing seasons and tested on the fourth for each fold. The results show that the model can successfully predict Sentinel-2-derived GAI with a cross-validation average R2=0.88 and RMSE=0.74, outperforming methods relying on radiative transfer model (e.g., the Water Cloud model) inversion. The model is also particularly effective compared to non-recurrent regression models, such as Random Forest and Multi-layer Perceptron, over long temporal gaps in the GAI time series, i.e., 30 to 60 days (15 to 30% of the growing season), a common occurrence in Belgium and many other parts of the world. These promising results pave the way for the generation of accurate, dense GAI time series throughout the growing season, allowing for timely crop monitoring in cloud-prone regions

Synthesis of Amino-acids - Alkylation of Aldimine and Ketimine Derivatives of Glycine Ethyl-ester Under Various Phase-transfer Conditions

The Sciff base derived from glycine ethyl ester and p-chlorobenzaldehyde can be alkylated by the ion-pair extraction method as well as under catalytic liquid-liquid or solid-liquid phase-transfer conditions. This imine is compared with the corresponding benzophenone Schiff base