Process integration study Kraft and Magnefite pulp and paper mill

Energy use is an indicator of economic growth. However, high energy intensity has two main disadvantages. First, low energy efficiency increases a country’s dependence on other countries, especially when the country lacks energy sources. Second, if the country’s energy needs are met using traditional fossil fuels, this increases its CO2 emissions and reduces its air quality. Improving energy efficiency and reducing energy intensity are essential to reach the sustainability targets. This paper investigates the determinants of energy use in Turkiye for the period 1991–2019 by taking a dual approach. First, utilizing the Tapio decoupling factor, the decoupling factor analysis is not only being done for total energy use and real GDP, but also for industrial energy use and industrial income. Second, the factors determining the country’s total energy use are also examined, followed by an investigation of the indicators of energy use in the industry sector, which is highly energy intensive. For the industrial sector, two different decomposition analyses are performed and results are compared. The refined Laspeyres index method is adopted, and for each analysis, three main factors are considered. The empirical findings demonstrate that the income effect and population effect increased Turkiye’s total energy use, whereas the energy intensity effect decreased it. The first decomposition analysis for the industrial energy use reveals partly contrasting results with the previously published articles. For the industry sector, the second analysis show that productivity and employment increased Turkiye’s sectoral energy use; however, the sector’s energy intensity reduced it. Turkiye achieved some success in terms of reducing energy intensity at the sectoral and aggregate levels; however, as the findings of the present study demonstrate, further efforts are needed to lessen the country’s energy dependence and also to achieve future environmental sustainability targets. Trends relating to the determining factors in total and sectoral energy use are also compared in this paper, and some policy implications are presented.

Building energy use in China: Ceiling and scenario

AimThe frequency of different body sizes in an ecological community (the individual size distribution, or ISD) is a key link between the number of individual organisms present in a community and community function—total biomass or total energy use. If the ISD changes over time, the dynamics of community function may become decoupled from trends in abundance. Understanding how, and how often, the ISD modulates the relationship between abundance, biomass and energy use is of critical importance to understand biodiversity trends in the Anthropocene. Here, we conduct the first macroecological‐scale analysis of this type for avian communities.LocationNorth America, north of Mexico.Time Period1989–2018.Major Taxa StudiedBreeding birds.MethodsWe used species' traits to generate annual ISDs for bird communities in the North American Breeding Bird Survey. We compared the long‐term trends in total biomass and energy use to the trends generated from a null model of an unchanging ISD.ResultsTrends in biomass have been evenly split between increases and decreases, but the trends predicted by the null model were dominated by decreases. A substantial number of communities have undergone a shift in the ISD favouring larger bodied species, resulting in a less negative trend in biomass than would be expected had the ISD remained static. Trends in energy use more closely paralleled the null model.Main ConclusionsTaking changes in the ISD into account qualitatively changes the continental‐scale picture of how biomass and energy use have changed over the past 30 years. For North American breeding birds, shifts in species composition favouring larger bodied species may have partially offset declines in standing biomass driven by losses of individuals over the past 30 years.

Sustainable High-rises

In order to adequately assess energy policies and set clear objectives, a key preliminary step is to know the energy use patterns of the different countries. This paper estimates the evolution of the total energy use over the period 1995–2015 in four European Union (EU) countries, the Czech Republic, Hungary, Italy, and Spain, representative of two different energy patterns, the “Southern” one and the “Eastern” one. For doing so, we employ a Multi-Regional Input Output (MRIO) model. In difference with previous studies, in addition to differentiate between domestic and foreign use we distinguish whether this energy is produced domestically or abroad. The results obtained show a certain convergence in energy intensity across the four countries examined because of the radical transformations experienced by the Czech Republic and Hungary. Nonetheless, energy intensities are still substantially higher in Eastern than in Southern countries which confirms that the first group of countries have still a long road to go, especially regarding the incentives that their industries have to use energy efficiently. Taking our decomposition of total energy use, the reductions in total energy use were mainly caused by a high decrease in the importance of the domestic use of energy produced domestically. At the same time, a growing importance of the role played by the energy produced abroad was observed. These trends confirm the great importance of global value chains and the steady internalization of energy use. This methodology could be further applied to other countries.

The relationship between operational energy demand and embodied energy in Dutch residential buildings

About 22% of total energy consumption in the U.S. is attributed to residential usage. To assess home energy use performance and propose energy-efficiency upgrades, knowledge of overall energy use is not sufficient, and the device-level, energy use, especially about the heating and cooling equipment, is usually required. The home energy use by Heating, Ventilating, and Air Conditioning (HVAC) systems approximately accounts for 48% of the total energy use per household, so decomposing overall energy use data into HVAC energy use weighs more values. However, existing methods to disaggregate HVAC use primarily rely on sensors or meters at either device-level or central power-level, and thus hinder the utilization for homeowners. Alternatively, for households, information about monthly utility usually is accessible and may be utilized to attain HVAC energy use through data mining techniques. In this study, the multivariate polynomial regression models of the month cooling and heating energy use were conducted based on overall monthly energy use, home profiles, and monthly weather data. The primary dataset used for training and testing the model was from the Pecan Street home energy use dataset. The resultant polynomial regression models, which achieved R2 value with 81.8% and 92.8% for cooling and heating energy usage prediction, respectively. These models may serve as a tool for homeowners to provide estimated HVAC energy use upon very few user input data and then support the building energy efficiency improvement actions, such as building retrofits, equipment upgrades, and energy feedback interventions.

Herschel Finds Less Dark Matter but More Stars

Energy use pattern and optimization of energy required for broiler production using data envelopment analysis

As quantum computers increase in size, the total energy used by a quantum data center, including the cooling, will become a greater concern. The cooling requirements of quantum computers, which operate at temperatures near absolute zero, are determined by computing system parameters, including the number and type of physical qubits, the packaging efficiency of the system, and the split between circuits operating at cryogenic temperatures and those operating at room temperature. When combined with thermal system parameters such as cooling efficiency and cryostat heat transfer, the total energy use can be determined using a first-principles energy model. These models show that cooling of quantum computers differs in two fundamental ways from conventional data centers: (1) the energy required for cooling is much greater than the energy required for computation, and (2) the cooling loads are sensitive to the computational architecture. The temperature requirements for different qubit types can change energy requirements by orders of magnitude. Power use and computational power, as quantified by quantum volume, are analytically correlated. Approaches are identified for minimizing energy use in integrated quantum systems relative to computational power. Designing a sustainable quantum computer will require both efficient cooling and system design that minimizes cooling requirements.

For a long period, the energy use for domestic hot water (DHW) in non-residential buildings has been neglected. As energy use for space heating, ventilation, and lightning has been reduced through the years, the energy share for DHW production has increased. With today’s focus on energy efficient buildings, the DHW systems must not be left behind the other fields to obtain a holistic approach to energy efficient buildings. To develop more energy efficient DHW systems for non-residential buildings, more knowledge is needed regarding draw-off durations, flow rates, and water temperatures from different draw-off types.This paper presents high frequency measurement results in two Danish office and educational buildings, which show that three DHW systems only utilize between 3.4 and 7.5 % of the total energy used for DHW production. Above 85 % of the total energy use is lost from the recirculation circuit to secure a high hot water temperature at the draw-off point. However, temperature measurements at the draw-off points show that a significant share of the heat is lost from pipes not covered by recirculation. One reason is draw-off durations are approximately 50 % of the time below 10 s at washbasins and kitchen sinks. Another reason is actual maximum flow rates are far from the calculated design flow.The high frequency measurements at multiple draw-off points give essential knowledge of use patterns for washbasins, kitchen sinks, and service sinks in non-residential buildings. This knowledge can contribute to developing energy efficient DHW systems and new types of DHW production.

Developing a landscape of urban building energy use with improved spatiotemporal representations in a cool-humid climate

It was mentioned in Chap. 1 that a significant portion of the total energy use is for temperature control and lighting in living and working spaces. Energy use for lighting purposes comprises between 20 and 50 % of the total energy used in homes, and varies appreciably with both location and time of the year, of course. This is expected to decrease substantially as the result of the use of fluorescent and light-emitting-diode (LED) devices in the future.

Buildings are at the locus of three trends driving China's increased energy use and emissions: urbanization, growing personal consumption, and surging heavy industrial production. Migration to cities and urban growth create demand for new building construction. Higher levels of per-capita income and consumption drive building operational energy use with demand for higher intensity lighting, thermal comfort, and plug-load power. Demand for new buildings, infrastructure, and electricity requires heavy industrial production. In order to quantify the implications of China's ongoing urbanization, rising personal consumption, and booming heavy industrial sector, this study presents a lifecycle assessment (LCA) of the energy use and carbon emissions related to residential and commercial buildings. The purpose of the LCA model is to quantify the impact of a given building and identify policy linkages to mitigate energy demand and emissions growth related to China's new building construction. As efficiency has become a higher priority with growing energy demand, policy and academic attention to buildings has focused primarily on operational energy use. Existing studies estimate that building operational energy consumption accounts for approximately 25% of total primary energy use in China. However, buildings also require energy for mining, extracting, processing, manufacturing, and transporting materials, as well as energy for construction, maintenance, and decommissioning. Building and supporting infrastructure construction is a major driver of industry consumption--in 2008 industry accounted for 72% of total Chinese energy use. The magnitude of new building construction is large in China--in 2007, for example, total built floor area reached 58 billion square meters. During the construction boom in 2007 and 2008, more than two billion m{sup 2} of building space were added annually; China's recent construction is estimated to account for half of global construction. Lawrence Berkeley National Laboratory (LBNL) developed an integrated LCA model to capture the energy and emissions implications of all aspects of new buildings from material mining through construction, operations, and decommissioning. Over the following four sections, this report describes related existing research, the LBNL building LCA model structure and results, policy linkages of this lifecycle assessment, and conclusions and recommendations for follow-on work. The LBNL model is a first-order approach to gathering local data and applying lifecycle assessment to buildings in the Beijing area--it represents one effort among a range of established, predominantly American and European, LCA models. This report identifies the benefits, limitations, and policy applications of lifecycle assessment modeling for quantifying the energy and emissions impacts of specific residential and commercial buildings.

Effects of tillage method and crop rotation on non-renewable energy use efficiency for a thin Black Chernozem in the Canadian Prairies