INTRODUCTION: Sufficient high quality dietary protein intake is required to prevent or treat sarcopenia in elderly people. Therefore, the intake of specific protein sources as well as their timing of intake are important to improve dietary protein intake in elderly people.OBJECTIVES: to assess the consumption of protein sources as well as the distribution of protein sources over the day in community-dwelling, frail and institutionalized elderly people.METHODS: Habitual dietary intake was evaluated using 2- and 3-day food records collected from various studies involving 739 community-dwelling, 321 frail and 219 institutionalized elderly people.RESULTS: Daily protein intake averaged 71 ± 18 g/day in community-dwelling, 71 ± 20 g/day in frail and 58 ± 16 g/day in institutionalized elderly people and accounted for 16% ± 3%, 16% ± 3% and 17% ± 3% of their energy intake, respectively. Dietary protein intake ranged from 10 to 12 g at breakfast, 15 to 23 g at lunch and 24 to 31 g at dinner contributing together over 80% of daily protein intake. The majority of dietary protein consumed originated from animal sources (≥60%) with meat and dairy as dominant sources. Thus, 40% of the protein intake in community-dwelling, 37% in frail and 29% in institutionalized elderly originated from plant based protein sources with bread as the principle source. Plant based proteins contributed for >50% of protein intake at breakfast and between 34% and 37% at lunch, with bread as the main source. During dinner, >70% of the protein intake originated from animal protein, with meat as the dominant source.CONCLUSION: Daily protein intake in these older populations is mainly (>80%) provided by the three main meals, with most protein consumed during dinner. More than 60% of daily protein intake consumed is of animal origin, with plant based protein sources representing nearly 40% of total protein consumed. During dinner, >70% of the protein intake originated from animal protein, while during breakfast and lunch a large proportion of protein is derived from plant based protein sources.
The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
The denim industry faces many complex sustainability challenges and has been especially criticized for its polluting and hazardous production practices. Reducing resource use of water, chemicals and energy and changing denim production practices calls for collaboration between various stakeholders, including competing denim brands. There is great benefit in combining denim brands’ resources and knowledge so that commonly defined standards and benchmarks are developed and realized on a scale that matters. Collaboration however, and especially between competitors, is highly complex and prone to fail. This project brings leading denim brands together to collectively take initial steps towards improving the ecological sustainability impact of denim production, particularly by establishing measurements, benchmarks and standards for resource use (e.g. chemicals, water, energy) and creating best practices for effective collaboration. The central research question of our project is: How do denim brands effectively collaborate together to create common, industry standards on resource use and benchmarks for improved ecological sustainability in denim production? To answer this question, we will use a mixed-method, action research approach. The project’s research setting is the Amsterdam Metropolitan Area (MRA), which has a strong denim cluster and is home to many international denim brands and start-ups.
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
“Being completely circular by 2050” that is the goal for the Dutch economy. The transition towards the circular and biobased economy for energy and materials is essential to reach that goal. Sustainably produced materials based on renewable sources like biomass should be developed. One of the industries which recognizes the need for transition is the building industry. Currently, there are a couple of biobased building concepts available which claim to be more than 95% biobased. Since the current resins and adhesives, used to produce panel boards (like cross laminated timber (CLT)), are all produced synthetically, one of the missing links for the building industry to become 100% biobased are biobased resins and adhesives (and binders). In literature, there are several solutions described for resins/adhesives/binders which are based on the biomolecules lignin and cellulose which are abundantly present in fibrous biomass, but these products are not (yet) available on the market. At the same time, there are several fibrous biomass side streams available for which higher added value applications are demanded. These side streams are perfect sources of lignin and cellulose and are, therefore, very suitable sources to form the basis for biobased resins/adhesives/binders. However, they need modification to obtain the desired functionalities. The problem statement of this project, based on the request for valorization of fibrous side streams and the need for biobased building materials, is “How can we valorize fibrous biomass (side streams) into biobased building applications.” This problem statement is translated into the research goal. The aim of this research is to develop a biobased resin, adhesive or binder for the production of panel boards based on the side streams of fibrous/lignocellulosic biomass which meets the requirement of the building industry with respect to VOC emissions, and water resistance so that it contributes to a healthy living environment.
