A novel cost-optimizing demand response control for a heat pump heated residential building

The present article describes the integration of a data-driven predictive demand response control for residential buildings with heat pump and on-site energy generation. The data driven control approach schedules the heating system of the building. In each day, the next 24 hours heating demand of buildings, including space heating and domestic hot water consumption, are predicted by means of a hybrid wavelet transformation and a dynamic neural network. Linear programming is implemented to define a cost-optimal schedule for the heat pump operation. Moreover, the study discusses the impact of heat demand prediction error on performance of demand response control. In addition, the option of energy trading with the electrical grid is considered in order to evaluate the possibility of increasing the profit for private householders through on-site energy generation. The results highlight that the application of the proposed predictive control could reduce the heating energy cost up to 12% in the cold Finnish climate. Furthermore, on-site energy generation declines the total energy cost and consumption about 43% and 24% respectively. The application of a data-driven control for the demand prediction brings efficiency to demand response control.     

Simulation of the heating performance of the Kang system in one Chinese detached house using biomass

A traditional Chinese heating system, the Chinese Kang, is used by 175 million people in detached houses in the rural regions of China, especially in Northeast China. This system utilizes biomass such as corn stalks, straw, corncob and energy plants as the heat sources. The objective of this paper is to establish a set of models to simulate the energy performance of the Kang heating system in one Chinese detached house. An experimental field study was carried out to collect practical parameters in a newly constructed Chinese detached house. The dynamic performance of the Kang heating system was simulated by using IDA-ICE 4.0 embedded with an empirical model built in the field study. The results show that the simulation can obtain good overall agreement with the field measurement results. It was confirmed that the Kang model created by IDA-ICE 4.0 is capable of simulating the performance of the Kang system and of calculating energy consumption in the detached house. Moreover, the result reveals that the thermal environment in the present Chinese detached house is still poor during the severe cold season if only the Kang system is used to heat the whole house.     

Sustainable PLM through Intelligent Products

The basis for Sustainable PLM is enabling Intelligent Products to communicate between themselves and with other systems in order to reduce CO₂ emissions, energy usage, and environmental damage. This type of communication between Intelligent Products and other information systems with the objective of reducing environmental impacts is an example of closed-loop product lifecycle management. The objective of the paper is to outline an architectural information system model for implementing Sustainable PLM based on experiences from a number of real-life applications. The technological challenge for developing Sustainable PLM is the high number and variety of information systems that need to communicate over organizational limits and over time. In the paper we describe how Sustainable PLM has been implemented using Intelligent Products in a number of representative real-life applications.     

An adaptive fuzzy control system to maximize rough turning productivity and avoid the onset of instability

This paper presents a new method to improve cutting efficiency for steel rough turning. To date, most efforts aimed at improving productivity during cutting operations have concentrated on optimizing material handling to and from the machinery. Here, the focus is on improving the efficiency of the turning operation itself. The approach is to control feed rate to raise machine power to a maximum safe level while avoiding the onset of cutting instability. The measure of machine power comes directly from the spindle motor and is held below the cutting machine??s power capacity. Detecting the onset of instability relies on interpreting data that come from installed instrumentation. A fuzzy inference system processes the inputs and makes the final control decisions. The prototype system was tuned using data collected in a variety of cutting situations. Subsequent testing of the tuned control system showed that it was capable of successfully maximizing power usage while still avoiding the onset of instability.     

Exceedance probability estimation for a quality test consisting of multiple measurements

The purpose of this study was to develop methods for exceedance probability estimation in the case of highly scattered measurement sets. The situation may occur when product quality is verified with several test samples, and thus, traditional point prediction based modelling methods are not sufficient.Density forecasting methods are needed when not only the mean but also the deviance and the distribution shape of the response depend on the explanatory variables. Furthermore, with probability predictors, the ranking methods for the model selection should be chosen carefully, when models trained with different methods are compared.In this article, the impact toughness of the steel products was modelled. The rejection probability in Charpy-V quality test was predicted with mean and deviation models, distribution shape model and quantile regression model. The proposed methods were employed in two steel manufacturing applications with different distributional properties.     

Effects of alfa-hydroxy-isocaproic acid on body composition, DOMS and performance in athletes

Background

The effect of moderate dehydration and consequent fluid replenishment on short-duration maximal treadmill performance was studied in eight healthy, fit (VO_2max = 49.7 ± 8.7 mL kg^-1 min^-1) males aged 28 ± 7.5 yrs.

Methods

The study involved a within subject, blinded, crossover, placebo design. Initially, all subjects performed a baseline exercise test using an individualized treadmill protocol structured to induce exhaustion in 7 to 10 min. On each of the three subsequent testing days, the subjects exercised at 70-75% VO_2max for 60 min at 29-33°C, resulting in a dehydration weight loss of 1.8-2.1% body weight. After 60 min of rest and recovery at 22 C, subjects performed the same treadmill test to voluntary exhaustion, which resulted in a small reduction in VO_2max and a decline in treadmill performance by 3% relative to the baseline results. Following another 60 min rest and recovery, subjects ingested the same amount of fluid lost in the form of one of three lemon-flavored, randomly assigned commercial drinks, namely Crystal Light (placebo control), Gatorade^® and Rehydrate Electrolyte Replacement Drink, and then repeated the treadmill test to voluntary exhaustion.

Results

VO_2max returned to baseline levels with Rehydrate, while there was only a slight improvement with Gatorade and Crystal Light. There were no changes in heart rate or ventilation with all three different replacement drinks. Relative to the dehydrated state, a 6.5% decrease in treadmill performance time occurred with Crystal Light, while replenishment with Gatorade, which contains fructose, glucose, sodium and potassium, resulted in a 2.1% decrease. In contrast, treatment with Rehydrate, which comprises fructose, glucose polymer, calcium, magnesium, sodium, potassium, amino acids, thiols and vitamins, resulted in a 7.3% increase in treadmill time relative to that of the dehydrated state.

Conclusions

The results indicate that constituents other than water, simple transportable monosaccharides and sodium are important for maximal exercise performance and effective recovery associated with endurance exercise-induced dehydration.     

Modelling of crystal growth of KDP in a 100 dm suspension crystallizer using combination of CFD and multiblock model

In this study a combination of computational fluid dynamics (CFD) and multiblock model is used for modelling crystal growth in a 100 dm³ suspension crystallizer equipped with two turbine impellers. Local hydrodynamics and crystal suspension densities were modelled using CFD. Simulation results were compared with experimental results to verify flow profile and slip velocities (Hatakka et al., 2008, 2009), and classification of crystals. Results from CFD simulations were then translated to a proper form and used as input data for the multiblock model.     

Review of sub-3 nm condensation particle counters,calibrations, and cluster generation methods

This review discusses the developments in aerosol instrumentation that have led to the current vapor condensation based instruments capable of detecting sub-3?nm particles. We begin from selected reports prior to the year 1991, which have advanced the technology or understanding in condensation particle counting toward sub-3?nm sizes, and continue to more in depth review of the past efforts after 1991. We discuss how the developments in the calibration methods have progressed the development of particle counting techniques, and review briefly the sub-3?nm calibration experiments and cluster production methods used in calibration experiments. Based on these reviews, we identify several technological and scientific advances for the future to improve the accuracy, understanding, and technology of sub-3?nm particle counting.

Copyright © 2019 American Association for Aerosol Research     

Growth and surface engineering of vertically-aligned low-wall-number carbon nanotubes

A novel catalyst of cobalt supported by single crystal MgO was prepared by atomic layer deposition and used for carbon nanotube growth. With CO as carbon source, vertically-aligned carbon nanotubes with predominant double-walled (82%) were produced at 700 °C. Similar carbon nanotube array with a majority of single-walled tubes (62%) was produced at 900 °C using methane as the carbon source. Due to their high flexibility compared with multi-walled carbon nanotubes, the low-wall-number carbon nanotube array could form a 3-dimensional honeycomb-like network when being spread with acetone.