Examining the Relationship between Energy Consumption and Unfavorable CO2 Emissions on Sustainable Development by Going through Various Violated Factors and Stochastic Disturbance–Based on a Three-Stage SBM-DEA Model

The article applies a three-stage Slacks-Based Measure-Data Envelopment Analysis (SBM-DEA) pattern to examine the relationship between energy consumption and unfavorable CO2 emissions on green sustainable development, for the 11 cities of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) during 2010–2016, by going through various violated factors and stochastic disturbance. Labor, capital and energy resource are chosen as input variables, while GDP and CO2 emission as output variables. During the three phases consisting of the SBM-DEA model (first stage and third stage) and SFA analysis (second stage), CO2 emission is considered as an unfavorable outcome, while stochastic statistical disturbances and external environmental influences are identified. The results show that the average efficiency of the GBA cities is 0.708, with only Shenzhen, Macao SAR and Hong Kong SAR having an efficiency of 1 during the whole study period. Based on the findings, suggestions are made for the GBA cities’ sustainable development aspects.

Intelligent Control on Urban Natural Gas Supply Using a Deep-Learning-Assisted Pipeline Dispatch Technique

Natural gas has been attracting increasing attentions all around the world as a relatively cleaner energy resource compared with coal and crude oil. Except for the direct consumption as fuel, electricity generation is now another environmentally-friendly utilization of natural gas, which makes it more favorable as the energy supply for urban areas. Pipeline transportation is the main approach connecting the natural gas production field and urban areas thanks to the safety and economic reasons. In this paper, an intelligent pipeline dispatch technique is proposed using deep learning methods to predict the change of energy supply to the urban areas as a consequence of compressor operations. Practical operation data is collected and prepared for the training and validation of deep learning models, and the accelerated predictions can help make controlling plans regarding compressor operations to meet the requirement in urban natural gas supply. The proposed deep neutral network is equipped with self-adaptability, which enables the general adaption on various temporal compressor conditions including failure and maintenance.

Primordial mimicry induces morphological change in Escherichia coli

The morphology of primitive cells has been the subject of extensive research. A spherical form was commonly presumed in prebiotic studies but lacked experimental evidence in living cells. Whether and how the shape of living cells changed are unclear. Here we exposed the rod-shaped bacterium Escherichia coli to a resource utilization regime mimicking a primordial environment. Oleate was given as an easy-to-use model prebiotic nutrient, as fatty acid vesicles were likely present on the prebiotic Earth and might have been used as an energy resource. Six evolutionary lineages were generated under glucose-free but oleic acid vesicle (OAV)-rich conditions. Intriguingly, fitness increase was commonly associated with the morphological change from rod to sphere and the decreases in both the size and the area-to-volume ratio of the cell. The changed cell shape was conserved in either OAVs or glucose, regardless of the trade-offs in carbon utilization and protein abundance. Highly differentiated mutations present in the genome revealed two distinct strategies of adaption to OAV-rich conditions, i.e., either directly targeting the cell wall or not. The change in cell morphology of Escherichia coli for adapting to fatty acid availability supports the assumption of the primitive spherical form.

IMPROVING THE ENERGY EFFICIENCY OF BUILDINGS BY PROVIDING BETTER PROTECTIVE STRUCTURES BASED ON THE UNIVERSITY'S KNOWLEDGE HUB

BACKGROUND AND OBJECTIVES. Modern university buildings use a large number of resources, such as heat, cold and hot water, as well as electricity, which is the main consumed energy resource and is used for lighting, office equipment, ventilation and air conditioning systems. In order to improve the energy efficiency of university buildings, it is necessary to carry out heat and energy modernization of internal and external envelopes, which will allow for internal billing and qualitative analysis of consumption, which contribute to prompt decision-making on heat and energy modernization of the premises.METHODS. To assess the potential for improving the energy efficiency of buildings by improving the quality of protective structures, methods for assessing the temperature and humidity conditions of multilayered enclosing structures in a wide range of humidity under stationary boundary conditions were developed on the basis of the University Hub of Knowledge.FINDINGS. On the basis of the University Hub of Knowledge, Kyiv National University of Technologies and Design, the moisture content profile was calculated for the general estimation of the moisture condition for building No. 4, the planes of the highest moisture content were determined to find the most dangerous, from the moisture condition point of view, section of the structure, the calculation of the enclosing structure modernization according to the maximum allowable moisture condition for the analysis of moisture accumulation in the coldest month of the year was made.CONCLUSION. The advantage of the proposed method of increasing the energy efficiency of buildings by improving the quality of protective structures based on the University Hub of Knowledge is the possibility of calculation in a wide range of moisture content of materials, including supersorption moisture zone, as well as applicability to structures with multizone condensation of moisture. The clarity and simplicity of the proposed method makes it available for the practical implementation of energy efficiency improvements in all university buildings.

Building Performance Simulations and Architects against Climate Change and Energy Resource Scarcity

In Europe, 40% of the total energy is consumed by buildings; in this sense, building performance simulation tools (BPSTs) play a key role; however, the use of these tools by architects is deficient. Therefore, this study aims to detect the architects’ perception on BPSTs. To this end, an online survey was conducted to determine the selection criteria of these BPSTs and non-users, to investigate the reasons for not using the tools. The outcomes showed that there was a wide gap between architects and the management of simulation programs in Spain, mainly due to the lack of training. BPSTs are described as a kind of intellect amplifiers, as they are perceived as powerful allies between professors and students of architecture and between architects and architectural design; therefore, through BPSTs, sustainability is taken very much into consideration to make buildings more energy efficient. Therefore, it is primarily concluded that further and higher education must undergo significant improvement to use simulations as part of the architectural design.

Modelling of Liquid-Liquid Equilibria Using NRTL and UNIFAC Equations in the Extraction of Bio-Oil-Based Phenolics Produced from the Pyrolysis of Sugarcane Bagasse

An exploration on renewable energy resources has been paid more attention due to the depletion of the fossil-based energy resource. In addition, their safe and environmentally friendly properties have attracted experts’ interest. One of the renewable energy resources is the bio-oil produced from sugarcane bagasse. The bio-oil was produced through a pyrolysis at 500°C. However, the produced bio-oil showed a high content of phenolics, c.a. 40-60%. A liquid-liquid extraction to remove the phenolics using methanol-chloroform solvents would be beneficial to improve the stability of the bio-oil as well as to obtain high purity phenolics. Modelling of the liquid-liquid equilibria in the extraction was then developed using NRTL and UNIFAC equations. The empirical quantitative data of phase equilibrium system were calculated on both the extract and raffinate phases. The lowest RMSD value of 0.043160 was obtained from the calculations using NRTL equation at an extraction temperature of 50°C. Thus, the most suitable model was achieved using NRTL equation.