Intelligent Measurement and Monitoring of Carbon Emissions for 5G Shared Smart Logistics

With the upgrading of logistics demand and the innovation of modern information technology, the smart logistics platform integrates advanced concepts, technologies, and management methods, maximizes the integration of logistics resources and circulation channels, and effectively improves the efficiency of logistics transactions, but its energy consumption problem is particularly prominent. The study of intelligent measurement and monitoring of carbon emissions in smart logistics is of great value to reduce energy consumption, reduce carbon emissions in buildings, and improve the environment. In this paper, by comparing and analyzing the accounting standards of carbon emissions and their calculation methods, the carbon emission factor method is selected as the method to study the carbon emissions of the smart logistics process in this paper. The working principle of each key storage technology in the smart logistics process is analyzed to find out the equipment factors affecting the carbon emission of each storage technology in the smart logistics process, and the carbon emission calculation model of each key storage technology is established separately by using the carbon emission factor method. Meanwhile, according to the development history of energy consumption assessment, the assessment process of different stages from logistics storage energy consumption assessment to smart logistics energy consumption assessment is analyzed, and based on this, a carbon emission energy consumption assessment framework based on 5G shared smart logistics is constructed. This paper applies the supply chain idea to define the smart logistics supply chain, constructs a conceptual model of the smart logistics supply chain considering carbon emissions, and at the same time combines the characteristics of the smart logistics supply chain to analyze the correlation between the carbon emissions of the smart logistics supply chain and the related social, environmental, and economic systems.

Fluorescence Characteristics of Coalbed Methane Produced Water and Its Influence on Freshwater Bacteria in the South Qinshui Basin, China

Production of coalbed methane (CBM) resources commonly requires using hydraulic fracturing and chemical production well additives. Concern exists for the existence of chemical compounds in CBM produced water, due to the risk of environmental receptor contamination. In this study, parallel factor method analysis (PARAFAC), fluorescence index, and the fluorescence area integral methods were used to analyse the properties of CBM produced water sampled from Shizhuang Block (one of the most active CBM-producing regions in the Qinshui Basin). A culture experiment was designed to determine the effect of discharged CBM produced water on microorganisms in freshwater. Water quality analysis shows the hydrochemistry of most water samples as Na-HCO3 type produced water of CBM appears as a generally weak alkaline (pH 8.69 ± 0.185) with high salinity, high alkalinity, and a high chemical oxygen demand (COD) value. Three individual components were identified by using parallel factor method analysis as humic-like components (C1), fulvic-like components (C2), and amino acid-like substances (C3). The fluorescence characteristic index comprehensively explains that the fluorescent substances in CBM produced water has the characteristics of a low degree of humification and a high recent self-generating source. The region integration results of characteristic peaks show that tyrosine-like and tryptophan-like materials account for more than 67% of fluorescent substances in CBM produced water. The addition of produced water from coalbed methane promotes the growth of freshwater bacteria, and this process is accompanied by the decrease of the proportion of fulvic acid, humic acid, and the increase of the proportion of soluble microbial metabolites. This paper proposes a convenient method for organic matter identification of CBM produced water and provides some theoretical support and reference for the improvement of CBM water treatment and utilization.

Expert System for Early Diagnosis of Tropical Malaria and Tertiana Malaria using Certainty Factor

Twano Health Center is one of the technical implementing units of the Jayapura City Health Office which organizes Health Efforts, but the constraints regarding the facilities and infrastructure of the Puskesmas are not yet adequate in supporting health services. Based on observations, the increase in the level of malaria sufferers in the Jayapura area is caused by parasites (protozoa) of the genus Plasmodium and the mode of transmission is through the bite of a female Anopheles mosquito. There are two types of malaria that are often experienced by Jayapura residents, namely Tropical Malaria (Plasmodium falciparum) and Tertiana (Plasmodium vivax). The purpose of this study is to create an expert system that can diagnose early diseases such as an expert or doctor using the Certainty Factor method which expresses belief in an event (fact or hypothesis) based on evidence or expert judgment in early diagnosis of Tropical Malaria and Tertiana. The research subjects taken were 5 patients who had symptoms of Malaria and 1 doctor to determine the symptoms of the disease) The expert system using the Certainty Factor method was used because this method was suitable in determining the disease, and the result was a percentage which was the level of accuracy in determining the patient's disease. Determination of the percentage is influenced by the MB value (a measure of the increase in confidence) and the MD value (a measure of the increase in distrust) obtained from the assessment of an expert. For data modeling using data flow diagrams (DFD) and website-based. Total accurate patient recapitulation results are 80% of the Expert System for Early Diagnosis of Tropical Malaria and Tertiana using Certainty Factor

Validation of The Cullender and Smith Method for Determining Pressure Loss in The Tubing in Gas Wells

The ability of the reservoir to deliver a certain quantity of gas depends both on the inflow performance relationship and the flowing bottom hole pressure. In order to determine the deliverability of the total well system, it is necessary to calculate all the parameters and pressure drops, one of which in the tubing. Calculation of pressure loss in the tubing is a very important parameter in the stability of fluid flow from the reservoir to the surface. The calculation of pressure loss in the tubing which is most widely used in the field is the Cullender and Smith Method. The purpose of this study is to validate why the Cullender and Smith method is most widely used in the field to determine the pressure loss in the tubing compared to other pressure loss in tubing methods. The methodology used in this study is calculating the pressure loss in the tubing with the Average Temperature and Deviation Factor Method, the Sukkar and Cornel Method, and the Cullender and Smith Method. After calculating the pressure loss in the tubing using each of these methods, then comparing the percent error of the calculation method with the results in the well. The data used in the calculation is the data from the MZ Field from 7 wells in the East Kalimantan area. The results of the average error percentage obtained from this study are the Average and Deviation Factor Method is 5.38%, the Sukkar and Cornell Method is 5.65%, and the Cullender and Smith Method is 3.83%. From this study, it can be said that the Cullender and Smith Method to be valid or the most accurate method for used in the field compared to other methods due to resulting the smallest percent error from the calculation.