Energy Consumption Parameters Research Articles

Development of Deflector Structure and Effects on Performance: Refrigerated Display Cabinet Application

Air flow is of great importance in terms of energy efficiency and homogeneity of the cooled package temperatures in open type refrigerated display cabinets (RDCs). Deflectors are critical components for air curtains which are provides air directioning to air channel and grill. A new deflector structure was investigated to increase the performance of the air curtain in this study. Current design deflector structure (Design 1) and new design deflector structure (Design 2) were compared for temperatures of air off-air on and energy consumption parameters. Airflow in the new design deflector structure was tested under Class-3 conditions (25 °C temperature and %60 relative humidity) with ISO 23953-2 2015 standard. According to the results obtained the data compared with the current design deflector structure. The thermal performance has been increased by reducing the temperature of the air off by 1.2 °C on average, the air on temperature by an average of 2.05 °C and the average package temperature by %17.8 with the new deflector design. Cooling performance was increased using new deflector structure with effective airflow between the air off and air on. In addition, thanks to Design 2, the annual energy consumption of RDC has been reduced by 10.17% and accordingly the CO2 emissions released to the environment have been reduced at the same rate.

Integrating building information modeling and life cycle assessment to analyze the role of climate and passive design parameters in energy consumption

With the Architecture, Engineering, and Construction industry representing a significant share of global energy consumption and greenhouse gas emissions, developing sustainable design and reducing buildings’ environmental impacts has become a priority over the past decades. Adopting building information modeling tools and implementing them into life cycle analysis techniques at the early stages of design has been an effective method for buildings sustainability evaluation. With our environment constantly changing, it is reasonable that the construction industry should also aim to adapt to these changes and use them. However, the role of local climate features and their effects on a building's energy output is often neglected. By using building information modeling–life cycle analysis integration techniques, this research aims to consider the role of climatic attributes along with some passive design strategies in the life cycle assessment of the building and see how it affects that building's energy performance. By only applying passive design strategies (not including heating, ventilation, and air conditioning system type), the models’ energy demand was reduced up to 30% of the original value. It is also witnessed that by using the proper equipment and construction materials that match the respective climate, up to 28% of the building's energy consumption during the operational phase, can be saved. Although insulation of the walls does reduce energy consumption values, it also contributes to more greenhouse gas emissions. The increase in greenhouse gas emissions was estimated to be around 3%, but since the insulation boards make up only 2% of the building's area, it is a considerable amount.

Design of Renewable Energy Consumption Scheduling Model Based on Quantitative Feedback Theory

In the renewable energy consumption scheduling, due to the large fluctuation of wind power output, the renewable energy consumption rate is low, and the energy consumption scheduling effect is poor. Therefore, a new renewable energy consumption scheduling model based on quantitative feedback theory is designed. For the first time, calculate the output response of wind power generation, obtain the output change rate of the wind farm at the time scale, and determine the response curve of wind power generation according to the power generation process is divided into three stages: the initial stage, the peak stage and the end stage, and the response output during the peak period, so as to obtain the transfer rate of output during the peak period. Build a mathematical model of renewable energy power generation, calculate the output state of wind power generation, and analyze the output characteristics of renewable energy storage system; Secondly, the objective weight coefficient of renewable energy consumption is determined by using quantitative feedback theory, the objective function of renewable energy consumption is constructed, the power injected by parameter nodes is determined, the quantitative feedback controller is designed by using loop shaping technology, the control structure of two degrees of freedom is determined, the objective function of renewable energy consumption is constructed, and the constraint range of renewable energy consumption capacity is determined according to static constraints such as current constraints and voltage constraints; Then, the quantitative feedback theoretical controller is designed, the input and output transfer functions of the consumption system are determined, and the renewable energy consumption scheduling model is constructed. The renewable energy consumption scheduling model is solved by particle swarm optimization through a variety of index parameters in the renewable energy consumption determined by the QFT controller. The experimental results show that the proposed model can effectively improve the renewable energy consumption rate and optimize the consumption scheduling effect.

Performance analysis of breadth-first search and depth-first search on MANET for health monitoring system

The development of mobile ad-hoc network (MANET) technology is increasing and is directly proportional to the need. MANET is a wireless network consisting of several nodes moving freely and without using the infrastructure. MANET can be applied in various fields such as the military, natural disasters, and health services. The health sector, especially the health monitoring system, gets more attention because it involves maintaining patient safety. However, wireless devices installed in health services have not been able to reach patients. This condition causes the required time and energy consumption to increase. Therefore, the breadth-first search (BFS) and depth-first search (DFS) methods are indispensable in determining the quality of service (QoS) in MANET. QoS parameters analyzed include packet delivery ratio (PDR), throughput, packet loss, delay, and energy consumption. This study aims to choose a health data traffic route that can improve QoS when transmitting data. The simulation results using the 2018 version of MATLAB show that BFS has a better performance than DFS on delay and energy consumption parameters. Meanwhile, DFS has better performance than BFS on PDR, throughput, and packet loss parameters.

Model for Managing the Integration of a Vehicle-to-Home Unit into an Intelligent Home Energy Management System.

Integration of vehicle-to-home (V2H) centralized photovoltaic (HCPV) systems is a requested and potentially fruitful research topic for both industry and academia. Renewable energy sources, such as wind turbines and solar photovoltaic panels, alleviate energy deficits. Furthermore, energy storage technologies, such as batteries, thermal, and electric vehicles, are indispensable. Consequently, in this article, we examine the impact of solar photovoltaic (SPV), microgrid (MG) storage, and an electric vehicle (EV) on maximum sun radiation hours. As a result, an HCPV scheduling algorithm is developed and applied to maximize energy sustainability in a smart home (SH). The suggested algorithm can manage energy demand between the MG and SPV systems, as well as the EV as a mobile storage system. The model is based on several limitations to meet households' electrical needs during sunny and cloudy weather. A multi-agent system (MAS) is undertaken to ensure proper system operation and meet the power requirements of various devices. An experimental database for weather and appliances is deployed to evaluate and control energy consumption and production cost parameters. The obtained results illustrate the benefits of V2H technology as a prospective unit storage solution.

Electrocoagulation of Corrugated Box Industrial Effluents and Optimization by Response Surface Methodology.

The electrocoagulation method using stainless steel anodes was applied to a corrugated cardboard box manufacturing plant’s wastewater with high COD content. The effects of current density, processing time and stirring speed on response functions were studied using the Response Surface Methodology (RSM). The removal efficiency of chemical oxygen demand (COD) and energy consumption were selected as response functions. The Central Composite Design (CCD) was chosen to explain the single and combined effects of independent variables on response functions. The COD concentration of the real industrial wastewater used in the experiments was 9130 mg L−1. The maximum COD removal efficiency of 91.6% is obtained with 19.78 Wh g−1 energy consumption. Current density and treatment time were effective parameters for both COD removal and energy consumption. Optimization for maximum COD removal with minimum energy consumption showed 80.9% of COD removal with 6.7 Wh g−1 of energy consumption at 15 mA cm−2, 700 rpm, and 28 min treatment time. The variables are optimized with a few experiments using the response surface method.Graphical abstract

Energy-Saving Manufacturing System Design with Two Geometric Machines

In recent years, due to increasing energy costs and deteriorating environmental conditions, manufacturing enterprises have become more concerned with energy efficiency. This paper is dedicated to minimizing energy consumption while maintaining the target productivity of a two-machine geometric serial line. To address this problem, we propose a nonlinear model under productivity constraints, where the system energy consumption consists of three components, namely, the setup, the idle, and the normal working energy consumption. By analyzing the properties of the energy consumption function, a new heuristic method, i.e., the energy consumption minimization (ECM) algorithm, is proposed to obtain the optimal solution. In addition, we extend the formulation to further investigate the energy saving problem with varied buffer capacities and propose an energy-saving (ES) algorithm to solve it based on the problem structure. Furthermore, the effectiveness of the algorithms is verified by designing numerical examples, and the effects of energy consumption parameters, buffer capacity, target productivity, and breakdown probabilities on the total energy consumption and optimal solution are presented.

Change rates and weight values of energy consumption parameters for light steel buildings in severe cold region

ABSTRACT Light steel buildings are widely used in rescue and disaster relief, but they consume a lot of energy to improve the indoor thermal environment due to their high thermal conductivity and poor thermal comfort. To clarify the relationship between various design parameters and their effect on energy consumption, the energy consumption impact of the design parameters including building orientation, aspect ratio, roof slope angle, window-wall ratio (WWR), and envelope structure of a typical light steel building in cold regions is comprehensively analyzed. The impact degree of parameters is determined by the simulation and analytic hierarchy process (AHP) analysis to calculate the weight value of the design parameters affecting the energy consumption. The results show that the consumption of energy increases with the increase of the aspect ratio, slope angle, WWR and heat transfer coefficient of envelope. The orientation of the minimum energy consumption is 180°, the weight less than 180° is positive, and that greater than 180° is negative. The envelope has the greatest impact on the energy consumption, with a weight of about 0.66, of which the wall and roof is about 0.40 and 0.26 respectively; Then the south WWR, slope angle, north WWR, aspect ratio and orientation are in turn, and the weights are about 0.12, 0.08, 0.07, 0.06 and ±0.02 respectively. The research results can optimize the building scheme and estimate the building energy consumption after optimization, which provides a reference for the energy-saving renovation of light steel buildings in severe cold regions.

Design of a novel micro‐Zone adaptive Energy‐Trust evaluation Active On‐Demand Vector protocol to optimize communication in challenging and attacked mobile network

SummaryHigh congestion, mobility, and energy restrictions are the critical challenges to mobile networks. The conventional routing protocols are unable to ensure effective and reliable communication in such dynamic and unpredictable situations. In this paper, a novel micro‐Zone adaptive Energy‐Trust evaluation Active On‐Demand Vector (mZ‐ETAODV) protocol is designed to provide routing solutions in extreme conditions. In this work, the complete network is divided into smaller zones of nxn. The extreme zone position‐based evaluation is conducted to handle the stability and to identify the next intermediate node. The zone‐covered neighbors are further computed under energy and trust measures to identify the most reliable neighbor. The proposed protocol is simulated under high mobility, congestion and man‐in‐middle attack situations. The analytical observations are recorded in terms of Packet Delivery Ratio (PDR) ratio, energy consumption, delay and communication loss parameters. The comparative results are generated against AODV, SAODV, PSAODV, PDS‐AODV, NVS‐AODV, iNVS‐AODV, Tripathy et al., DFMCRP, FBSSR, MBDP‐AODV, AGHA, and Singh et al. protocols. The experimentation is conducted in a highly challenging mobile network with high congestion, high mobility, energy restriction, existence of blackhole, grayhole, DDOS, and mixed attacks. These attacks are performed with 0% to 10% density, and analysis is conducted against PDR ratio, communication delay, route switching, dead node count, remaining energy, and attack detection rate measures. The analysis results identified that the proposed protocol reduced the communication delay and energy consumption and achieved significant gain in attack detection rate, network life and PDR ratio.

Experimental investigation on energy saving potential for thermally activated buildings integrated with the active cooling system

ABSTRACT A thermally activated building system (TABS) is the most promising technology for reducing heat gain, cooling demand, and improved indoor comfort inside the built environment. This study investigates the energy consumption and thermal environment performance of the cooling tower-based thermally activated building system (passive system) integrated with a chiller unit (active cooling system). Energy consumption and indoor environment parameters, such as heat flux, indoor air, wall, floor, and the operative temperature, were measured in two computer laboratories of the same size and orientation operated with a chiller unit and chiller-integrated passive system. This strategy involved pre-cooling the building thermal mass during unoccupied hours via TABS and continuously using the system while maintaining zone temperatures at the comfort region during the occupied period. The results show that chiller-integrated TABS has a maximum heat gain of 10 W/m2 and maintains a mean radiant temperature from 24°C to 26°C for 80% of the interval. Furthermore, due to the pre-cooling effects of TABS, chiller-integrated TABS has a peak monthly energy savings of 6% and a CO2 emission reduction of 8% compared to an active cooling system.

Research on Power System Joint Optimal Generation Scheduling Based on Improved Balance Optimizer

This article presents a power system joint optimization generation regulation method based on the improved balance optimizer, which takes the five factors of power system network loss, voltage offset, generation cost, fuel cost, and comprehensive pollution emission as the objective function and takes the internal power balance of the system, each generator set, generation capacity, generation flow, and up and down climbing as the constraints. Fully considering the current energy-saving development objectives and the impact of economic dispatching, taking stable and safe operation as the core, the power generation dispatching model is established by improving the balance optimizer. The model realizes the maximum power generation with the lowest energy consumption parameters and transitions from the original power generation energy consumption of the power system to the best energy-saving power generation energy consumption so that the power value of the system reaches the target balance and completes efficient dispatching. Simulation experiments show that the proposed method can ensure the most reasonable power load in both summer and winter. The average load in summer and winter is reduced from 254.78/mw to 205.36/mw, down about 19.39%, which can ensure the power generation stability of the power system. The average power generation cost after dispatching is 129,920 $/h, which is significantly improved by comparing with 131,225 $/h before dispatching and can realize certain environmental benefits.

Energy Prediction and Optimization Based on Sequential Global Sensitivity Analysis: The Case Study of Courtyard-Style Dwellings in Cold Regions of China

A great abundance of rural houses lacking design guidance exists in the cold regions of China, often accompanied by huge energy loss. Particularly, a courtyard-style dwelling (CSD) has more complex and diverse building elements than a common house, rendering the design optimization extremely costly. Sensitivity analysis (SA) can screen the significant parameters of energy consumption for prediction and optimization. In this paper, (1) the design variables related to CSDs and their data details were extracted; (2) a ranking of parameters sensitive to energy demand was formulated; (3) an energy prediction model was trained and (4) dual-objective optimization was carried out. Using the survey data from 150 units in nine villages, 25 control variables were extracted for sequential global sensitivity analysis (GSA). Thus, the ranking of sensitivity parameters was formulated with the two-stage-and-three-sort GSA method. Furthermore, an energy prediction model was then trained with Gaussian Process Regression (GPR) and compared with the other four high-precision models. Based on the obtained prediction model, optimization was then carried out on energy and economic concerns. Consequently, a GSA-based workflow for CSD optimization was proposed to help architectural designers figure out the most efficient energy-saving parameter strategy.

Evaluation of the total exergy and energy consumptions in residential sector in Qassim Region, Saudi Arabia

Economic crises face a new foe worldwide, along with the fact that the Kingdom of Saudi Arabia (KSA) is facing problems to sustain its role in international energy prospects due to unavoidable domestic challenges caused by a fast-growing population. The challenges are unemployment for the youngsters, two-thirds of the total population, and emerging demand for energy consumption at the domestic level at unsustainably high rates. Besides this, KSA spends a considerable amount, around 9% of the GDP, to subsidize the energy sectors (oil products and electricity). All these aspects call for a selective study to pinpoint the factors which can directly or indirectly influence to sustain the Saudi economy in the long run.To help cater to the growing demand for rigorous study on areas for energy rationalization in the Qassim region, the objectives of this work are to perform a thorough study of energy and exergy analysis of various electrical household appliances and exergy analysis of the residential sector (RS). In this study, an independent survey of 100 dwellings was conducted to collect data related to essential parameters of residential energy consumption. The collected data has been analyzed to determine energy consumption indicators and perform exergy analysis. The average monthly and yearly energy consumption per dwelling was between 30,832 to 36,166, and 1500 to 4500kWh, respectively. The average hourly electrical energy consumption during the working days was 4.12kWh, and during the curfew due to the Coronavirus (COVID-19) 4kWh. The breakdown of this end-use of residential energy is mainly distributed for air conditioning, water heating, lighting, and all other domestic appliances with 67.34%, 9.31%, 8.18%, and 15.17%, respectively. The calculated total energy efficiency of 142.9% for KSA in this study showed a significant discrepancy compared to the previously reported value of 77.52%. Calculated total exergy efficiency falls between 11.13% and 11.38% for the Qassim region. Energy policymakers can use clear energy consumption indicators and exergy efficiency present in this study. They can help to establish energy efficiency standards to be used for various economic sectors.

Machine Learning Models for the Prediction of Energy Consumption Based on Cooling and Heating Loads in Internet-of-Things-Based Smart Buildings

In this article, the consumption of energy in Internet-of-things-based smart buildings is investigated. The main goal of this work is to predict cooling and heating loads as the parameters that impact the amount of energy consumption in smart buildings, some of which have the property of symmetry. For this purpose, it proposes novel machine learning models that were built by using the tri-layered neural network (TNN) and maximum relevance minimum redundancy (MRMR) algorithms. Each feature related to buildings was investigated in terms of skewness to determine whether their distributions are symmetric or asymmetric. The best features were determined as the essential parameters for energy consumption. The results of this study show that the properties of relative compactness and glazing area have the most impact on energy consumption in the buildings, while orientation and glazing area distribution are less correlated with the output variables. In addition, the best mean absolute error (MAE) was calculated as 0.28993 for heating load (kWh/m2) prediction and 0.53527 for cooling load (kWh/m2) prediction, respectively. The experimental results showed that our method outperformed the state-of-the-art methods on the same dataset.

Configuration Generation Method of Ship End Program for Ship Energy Efficiency Management Platform

In order to solve the problem of collecting necessary data affecting fuel consumption and building an intelligent energy efficiency system, this paper makes an in-depth study on the management of cross ship energy efficiency on the basis of program configuration generation. Firstly, this paper studies three methods: technology-driven analysis, support vector machine method, and energy efficiency evaluation method. In order to improve the functions of energy management, energy efficiency analysis, and auxiliary decision-making, this paper constructs a comprehensive intelligent scheme for the best ship navigation energy efficiency from the aspects of ship operation guidance, navigation performance evaluation suggestions, and shipping management decision-making, so as to meet the higher-level management needs of interested parties for ship energy efficiency. Among them, energy efficiency online monitoring can collect energy efficiency data and serve the other three functions, mainly monitoring ship energy consumption, environment, navigation, and other parameters. Energy efficiency evaluation is based on the data collected by online monitoring, and the results of ship energy consumption are obtained by using data mining technology. Through experiments and research, under the background of green, efficient, and intelligent ships, ship shore collaboration is an effective way to improve the ship’s energy efficiency management ability. Intelligent ships provide a new development path for strengthening the application service of ship energy efficiency management system.

Effect of Amino Acid Taurine on Grass Carp Ctenopharyngodon idella Fingerlings during Salinity Stress

The present study was carried out to evaluate the effect of different levels of dietary taurine (0%, 1%, 2% and 3%) on grass carp Ctenopharyngodon idella fingerlings during salinity stress. Fishes (1.73-8.57 g) were fed a diet up to satiation twice daily for ten weeks in four treatments: T1, T2, T3 and T4. At the end of the feeding trial, fishes were stressed by exposure to 10 PSU salinity for 14 days during which fish survival rates, total protein, glucose, oxygen and energy consumption were observed. The results showed that there were no significant differences (P>0.5) in the survival rates between treatments, which ranged from 73.3% to 93.3%. Total protein and glucose were significantly (P≤0.05) improved by a dietary taurine diet after 14 days of salinity stress in T2 and T3. Taurine supplementation significantly (P≤ 0.05) influenced oxygen consumption with the highest value observed for T1 (255.8 mg O2/kg/hr) and T2 (213.6 mg O2/kg/hr), while the lowest value was in T3 (131.5 mg O2/kg/hr) and T4 (112.4 mg O2/kg/hr) after 14 days of salinity stress. Energy consumption rates were significant (P≤0.05) different between the treatments on the 14th day, T1 (0.85 kcal/kg/hr) significantly exceeded the other treatments, followed by T2 (0.72 kcal/kg/hr), T3 (0.44 kcal/kg/hr) and T4 (0.38 kcal/kg/hr). It appeared that taurine could be used as a feed supplement to confirm better energy consumption and blood biochemical parameters during salinity exposure of grass carp fingerlings with the optimal level of a 2% and 3% diet.

Modelling the Effect of Temperature on Power Generation at a Nigerian Agricultural Institute

The energy sector is considered one of the most sensitive sectors to climate change. Climate change has a considerable impact on environmental weather parameters such as temperature, humidity, radiation from the sun, precipitation, sunshine hours, wind direction, etc. These meteorological considerations have an impact on the electricity consumption rate. As a result, knowing the influence of weather conditions on energy demand and consumption is critical for adapting, planning, and forecasting the impact of changing climate on an organization's energy needs. Several factors influencing electricity consumption can be classified as economic, seasonal, and meteorological factors. This research aims to look at the influence of climate change on energy supply in a typical agricultural institute and utilize Artificial Neural Network (ANN) and Multivariate Linear Regression (MLR) models to predict the impact of changes in temperature on electricity generated. The approach used in this study includes: Creating a database of weather variables and energy demand or consumption parameters; analyzing and correlating electrical energy demand to weather variables, developing models - Multivariate Linear Regression (MLR) and Artificial Neural Networks (ANN) to forecast the impact of change in the weather variables on the electrical energy. “Average temperature” was seen to have the most influence on electrical energy with the highest correlation (r = 0.92 for 2015 and r = 0.86 for 2011 - 2018), while “Wind speed” had the least influence with the lowest correlation (r = 0.033 for 2011 - 2018). The ANN model was the best of the two models considered in this study. The mean squared error was reduced by 39% and 42% on test data and train data, respectively, indicating that ANNs outperformed the MLR model. Other measures, such as Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), showed that the ANN performed substantially better than the MLR. The results suggest that ANN models perform relatively well since the algorithm learns independently and develops a reasonably accurate representation of the dataset.

The optimal behavior of renewable energy resources and government's energy consumption subsidy design from the perspective of green technology implementation

Carbon emissions are a major problem in the industrial sector due to energy consumption. As a solution, this research first investigates the impact of energy consumption and shortfall on green technology implementation from 1000 textile firms in Punjab, Pakistan and for energy shortfall incorporates subsidy variables to make green technology implementation possible via a binary logistic regression model. Second, we develop a simulation-based optimization model to measure the optimal behavior of energy consumption parameters to clean the environment under green technology implementation. The results depict that renewable energy resources (coal, corn cobs, and wood) positively affect green technology implementation. Interestingly, energy shortfall (electricity and gas) is also impacted positively due to the move of industries towards renewable resources during this shortage period. We find a higher quantity of wood in optimal behavior analysis instead of coal and cobs due to its ease of availability. An optimal subsidy on electricity shortfall is found to be greater than the natural gas shortfall subsidy due to its higher shortfall. This study helps the government to decide the optimal energy shortfall for saving energy usage and to manage the optimal level of subsidies in order to overcome energy usage problems. It also benefits the textile industry in deciding over the optimal energy consumption level to make the environment clean in terms of green technology implementation.

Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel

Hard turning has become an attractive method of machining for most manufacturers in the last few years due to its low cost and superior surface quality compared to grinding. In this experimental study, the machinability of hardened steel under dry machining on a CNC lathe is undertaken to optimize the cutting parameters for minimum surface roughness and energy consumption with the cutting speed (320, 450, and 575), tool type (coated and uncoated), and feed rate (0.1, 0.18, and 0.26) as the input parameters. The Taguchi method, based on the L18 orthogonal array, the variance analysis, the signal-to-noise ratios, and the response surface methodology have been used to optimize surface roughness (Ra) and cutting power (Cp). Optimum cutting parameters and levels were determined, and the relationship between cutting parameters and output variables was analyzed with the aid of two-dimensional and three-dimensional graphics. The results show that the most effective parameter on the surface roughness was the tool type (78%), while the most effective parameter on energy consumption was the cutting speed (90%). The combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness. Besides, the impact of two-factor interactions of the feed rate-cutting speed and depth of cut-cutting speed appears to be substantial. The linear regression models were validated using confirmation tests. Finally, regression coefficients were determined as a mathematical model, and it was observed that this estimated model yielded results that were very similar to those achieved via real experiment (correlation values: 97.64% for surface roughness and 98.72% for energy consumption).

Comparative techno-economic evaluation of potential processing schemes for petroleum crude oil distillation

The two most commonly used schemes for crude oil distillation in operating refineries have been compared with two new schemes using two different crude oils for the energy consumption and energy cost parameters. Seven cases for each crude were conceptualized to carry out a realistic techno-economic evaluation of these schemes. Study results reveal the scope to reduce the ADC bottom stripping steam in 32.7–75% for the proposed scheme–I and 100% for scheme II. The furnace duty is reduced ~ 4.3–4.7% for the scheme–I and ∼ 4.6–6.15% for the scheme–II. Overall, the energy cost for crude oil distillation can be reduced in the range of 7.3–9.4% with scheme-I and 10.7–16.3% with scheme-II by implementing proposed schemes for revamping the existing and designing the new CDUs. The lower vapour rate in the rectification section and higher vapour rate in the stripping section of ADC in the proposed schemes compared to the conventional schemes suggests an opportunity to either increase the throughput without revamping the ADC or implement the proposed schemes to revamp existing AVUs without ADC modification and assistance in addressing the challenges associated with poor vapour liquid contact, lower tray efficiency, and fouling on tray decks. The structural topology of new schemes is similar to existing schemes, which ensure their easy and practically possible implementation for revamping the existing and designing the grass-root units CDUs to reduce energy costs and emissions significantly.