Minimize Energy Use Research Articles

Feasibility of natural wastewater treatment systems and life cycle assessment (LCA) for aquatic systems

Background: Natural wastewater treatment systems (NWTSs) in small villages are a major challenge for European water authorities. With growing social demands for environmental practices, evaluating the feasibility and environmental impact of low-cost treatment systems for small residential areas is essential. Methods: To address this challenge, this study was conducted to evaluate 10 NWTSs seasonally in rural areas of Bursa, Turkey. Authorities over the facilities permitted the examination of workable, low-cost effluent management options. Also, using Open-LCA software based on ReCiPe MidPoint (H) version 1.67, these plants’ effects on global warming, Phosphorus-depletion, human toxicity, marine eutrophication, and freshwater eutrophication were examined. Results: According to the LCA findings, Deydinler NWTS had a greater impact across all three effect areas (freshwater eutrophication, marine eutrophication, and human toxicity), ranging from 11 to 41%. Pinar and Yenice facilities, however, had 26% and 27% larger impacts in the same two impacts (marine eutrophication and human toxicity). These systems performed on average at 67%, 50%, and 58% chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) elimination, respectively. Conclusion: According to the findings, 10 NWTSs have a treatment efficiency of about 70%. NWTSs are possibilities for decentralized wastewater treatment in small residential areas that are both cost-effective and environmentally favorable. By treating organic pollution naturally, without chemicals, and with minimal energy use, they lessen their negative environmental effects. The main findings of this study will be useful for academics in determining future research areas and identifying whom they might consult to help design carbon footprint of NWTS and future carbon reduction objectives.

Long-term implementation of a model predictive controller for a hydronic floor heating and cooling system in a highly glazed house in Canada

This article presents the results of a 182 day implementation of a model predictive controller (MPC) in a test house in Ottawa, Canada. The MPC controls the timing of hydronic floor heating and cooling within the highly glazed house (13.5% south-facing window-to-floor ratio). The objectives of the MPC are to optimally control the on/off times of active heating and cooling to minimize energy use while also minimizing air temperature violations outside of a prescribed range. It was found that 71% of the days in the experimental period saw successful operation of the MPC towards these goals. Days that experienced overheating were found to be concentrated in January–February and days that experienced overcooling were found to be concentrated in March–May. Problematic decision making by the MPC was found to be rooted in 4 main issues: (1) global horizontal irradiance (GHI) is a bad indicator of passive solar on its own; (2) weather forecasting (specifically GHI forecasting) is critical to MPC success in highly glazed buildings; (3) the GHI history regressor used in the prediction model regularly influenced predictions illogically; and (4) the MPC lacked decision-making logic on the timing of hydronic floor cooling.

Optimal optical properties for smart glazed windows applied to residential buildings

The paper provides a systematic evaluation of the optimal optical characteristics of smart windows to maximize their energy efficiency benefits when deployed for residential buildings. The evaluation accounts for a wide range of design specifications and operation conditions of housing units including building orientation, window sizes, and climatic zones. Using integrated analysis aimed to minimize energy use required to heat and cool housing units, the optimal design and control settings are determined for two types of smart windows including those that can switch between only clear and dark tint states and those that can transition to multiple intermediate tint levels. The results from a series of sensitivity analyses show that the required design optical specifications for the smart windows could depend on several variables including primarily climate characteristics and window features including size, orientation, and U-value. Significant energy savings can be achieved by smart windows relative to the code-compliant static fenestration systems ranging from 21% in cold climates to 5% in warm climates. These energy savings can be achieved using stepped controls to set the tint levels of the smart windows. Additional albeit slight savings can be obtained through gradual controls for those smart glazing that have the capabilities to transition to intermediate tint levels.

Developing rumen mimicry process for biological ammonia synthesis

The ruminant rumen houses hyper-ammonia-producing bacteria (HAB) that produce ammonia with minimal energy use. Here we developed a mimicry process to produce bio-ammonia, a solution of ammonia and ammonium. The rumen microbes were used to ferment soybean (SYB), soybean protein isolate (SPI), and pepsin-hydrolysate (HP) for bio-ammonia production. The maximum bio-ammonia produced from SYB, SPI, and HP were 0.65, 1.2, and 1.1g/L, respectively. The presence of non-protein in SYB hindered bio-ammonia production and the processing of SYB to SPI and HP significantly (p < 0.05) increased bio-ammonia production. HP was converted to bio-ammonia quicker than SPI suggesting that enzymatic hydrolysis increases bioprocessing efficiency. Metagenomic analysis of a sample culture revealed that the HAB population is predominantly Klebsiella quasivariicola (73%), Escherichia coli (6%), and Enterobacter cloacae (6%). The bioprocessing steps developed would enable industrial ammonia production to achieve a low CO2 footprint.

Minimizing Energy Depletion Using Extended Lifespan: QoS Satisfied Multiple Learned Rate (ELQSSM-ML) for Increased Lifespan of Mobile Adhoc Networks (MANET)

Mobile Adhoc Networks (MANETs) typically employ with the aid of new technology to increase Quality-of-Service (QoS) when forwarding multiple data rates. This kind of network causes high forwarding delays and improper data transfer rates because of the changes in the node’s vicinity. Although an optimized routing technique to transfer energy has been used to lessen the delay and improve the throughput by assigning a proper data rate, it does not consider the objective of minimizing the energy use, which results in less network lifetime. The goal of the proposed work is to minimize the energy depletion in a MANET, which results in an extended Lifespan of the network. In this research paper, an Extended Life span and QSSM-ML routing algorithm is proposed, which minimizes energy use and enhances the network lifetime. First, an optimization problem is formulated with the purpose of increasing the network’s lifetime while limiting the energy utilization and stability of the path along with residual. Second, an adaptive policy is applied for the asymmetric distribution of energy at both origin and intermediate nodes. In order to achieve maximum network lifespan and minimal energy depletion, the optimization problem was framed when power usage is a constraint by allowing the network to make use of the leftover power. An asymmetric energy transmission strategy was also designed for the adaptive allocation of maximum transmission energy in the origin. This made the network lifespan extended with the help of reducing the node’s energy use for broadcasting the data from the origin to the target. Moreover, the node’s energy use during packet forwarding is reduced to recover the network lifetime. The overall benefit of the proposed work is that it can achieve both minimal energy depletion and maximizes the lifetime of the network. Finally, the simulation findings reveal that the ELQSSM-ML algorithm accomplishes a better network performance than the classical algorithms.

Energy-aware message distribution algorithm for enhance FANET pipeline surveillance reliability

Features such as the communication scheme, energy awareness, and task distribution amongst others are the key component that characterizes the Flying Ad-hoc Network (FANET). The operational efficiency in FANET surveying a specific region is affected by the nature of the UAVs' node placement, routing protocol, energy-aware task distribution, and node interaction amongst others. In this paper, Drone 1 (D1), Master Drone (DM), and Drone 2 (D2) were used to survey a pipeline of length 12.2 m. This paper aims at minimising energy use by drones during surveillance using energy-aware node exchange technique, task interaction and distribution scheme for each UAV. Due to fast energy depletion of DM due to packets aggregation, its election is based on the UAV with the highest energy before take-off. For two different simulations, 14,697.0 J and 14,836.6 J were obtained for DM. To avoid system failure due to fast energy loss of DM, the drones swapped positions and status. First swapping command comes up when DM loses 50% of its energy, while the second command occurs when it further loses 15%. Return to base threshold energy is computed for the three UAVs to avoid crash due to insufficient energy during surveillance. DM returns to base threshold energy for both single and double swapping simulation were 658.105 J and 652.456 J respectively. From the results obtained the algorithms were able to exchange nodes to maximize energy usage and perform an interaction-based task distribution for cooperative task sharing during surveillance. This translates into longer surveillance time and effective telemetry data aggregation.

Data mining and analysis for predicting electrical energy consumption

In this study paper, the feasibility of constructing a complete smart system for anticipating electrical power consumption is created, as electricity's market share is expected to expand over the future decades. Smart grids and smart meters will help utility companies and their customers soon. New services and businesses in energy management need software development and data analytics skills. New services and enterprises are competitive. The project's electricity consumers are categorized by their hourly power usage percentage. This classification was done using data mining (five algorithms in specific) and data analysis theory. This division aims to help each group minimize energy use and expenditures, encourage energy-saving activities, and promote consumer involvement by giving tailored guidance. The intended segmentation is done through an iterative process using a computer classification computation, post-analysis, and data mining with visualization and statistical methodologies.

Integrated Convex Speed Planning and Energy Management for Autonomous Fuel Cell Hybrid Electric Vehicles

Fuel cell hybrid electric vehicles (FCHEVs) have zero harmful emissions, fast refueling times, and long driving ranges. Autonomous FCHEVs add benefits such as collision avoidance and driver convenience yet increase vehicle energy usage due to sensors and computation. Thus, it is important to optimize vehicle speed trajectories and the energy management strategy (EMS) of autonomous FCHEVs to minimize energy use. This article uniquely proposes to achieve this goal using convex optimization, with detailed vehicle loss calculations that can be run in real time. The two novel approaches proposed are: 1) the successive method, which solves the speed problem then the EMS problem, and 2) the integrated method, which uniquely feeds back the fuel cell efficiency to the speed algorithm to solve the problem in an iterative integrated manner. The simulation results show that the integrated method uses 0.19% to 2.37% less hydrogen than the successive method on short drive cycles with varying accessory loads, and 10.12%–21.62% less hydrogen than an arbitrary constant speed profile. On longer real-world drive cycles, the integrated method reduces hydrogen use by 1.43% to 2.82% just through speed optimization. Compared to a dynamic programming benchmark, which is not implementable in real time, the integrated method uses less than 1% more hydrogen.

Mitigating an adoption barrier of reinforcement learning-based control strategies in buildings

Reinforcement learning (RL) algorithms have shown great promise in controlling building systems to minimize energy use, operational cost, and occupant discomfort. RL agents learn a control policy by interacting with the physical or simulated environment that represents building systems, occupants, and the outside world. Yet, a large amount of data is needed to learn a near-optimal control policy in the physical building, which requires months or years to collect. Moreover, an agent’s performance while training can be quite poor, causing occupant discomfort and additional costs. Learning in simulation does not have such real-world impacts, but differences between buildings, and indeed between simulation and physical buildings, potentially lead to poor performance when a policy learned in simulation is deployed in a physical building. This paper addresses part of the sim-to-real problem by training on one set of simulated (source) buildings and then deploying to a novel simulated (target) building. This approach significantly reduces the training cost of RL on the target building by 1) learning a large number of policies on prototype buildings, 2) evaluating these policies on historical data obtained from the target building’s environment and selecting the best ones according to the evaluation result, and 3) using the best policies to control the target building while continuing to learn. The proposed approach involves learning a diverse population of control policies using a novel diversity-induced RL algorithm, and policy clustering, evaluation, and selection techniques. Three case studies show our approach assigns policies to the target building that outperform the default controller by 4.0–30.4%, without sacrificing thermal comfort. Similarly, they outperform policies that are learned only on the target building (i.e., without transfer) by 24.9–74.9% and 16.2–72.2% before and after 500 months of training, respectively.

Developing and tuning a community scale energy model for a disadvantaged community

This work describes the development of a community-scale energy model for a mixed-use low-income community located in Huntington Beach, CA. An accurate community-scale energy model is useful for evaluating the use of limited capital resources used to invest in clean energy technologies. This work lays out the process of developing such a model while relying primarily on publicly available data and highlighting critical partnerships necessary for model development success. The primary contribution of this work is the demonstration of the process used to develop an accurate energy model for a disadvantaged community when minimal building and energy use data is available. The heart of the model is the physics-based community scale energy modeling platform URBANopt. Using a bottom-up load modeling approach, energy simulated energy use falls within 3% or less of aggregate annual utility data, and within 10% or less aggregate monthly utility data. The demonstrated model development and tuning process can be used by others to characterize other atypical communities, which may differ significantly from prototypical models.

Valorisation of food processing wastes using PEF and its economic advances – recent update

SummaryFood processing is an emerging industrial sector that applies new technologies to process foods efficiently. During processing, food industries produce a substantial amount of by‐products as waste. Disposal of these wastes remains a problem and causes environmental pollution and greenhouse gas emissions. However, novel food processing technology like pulsed electric field‐assisted extraction (PEFAE) has the potential to utilise these wastes as a source of bioactive compounds and extract valuable compounds in a shorter period with minimal use of solvents and energy. This review covers the basics of pulsed electric field (PEF) and its effect on bioactive compound extraction yield and quality. Furthermore, the article compares the PEF treatment energy and extraction efficiency with other non‐conventional extraction methods to better make the reader understand in terms of selecting the suitable processing method. However, the extraction yield and process efficiency depend highly on the processing parameters. Hence, the article also explains the various kinetic models involved in PEF‐assisted food waste valorisation to control the process parameters during treatment.

Butylene Glycol Used as a Sustainable Solvent for Extracting Bioactive Compounds from Camellia sinensis Flowers with Ultrasound-Assisted Extraction.

The research aims to assess the yield of bioactive compounds and their antioxidant activities obtained from tea flowers using an ultrasound-assisted extraction method with butylene glycol (BG-UAE) through Box-Behnken design. It investigates the bioactive compounds including the total phenolic content (TPC), total flavonoid content (TFC), and total tannin content (TTC) and analyzes their antioxidant activities, bioactive compound composition by liquid chromatography triple quadrupole tandem mass spectrometry, and their cellular activities via UAE and maceration using BG or ethanol as the solvent. Under optimal conditions, the values of the TPC, TFC, TTC, 1,1-diphenyl-2-picrylhydrazil radical scavenging assay, 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid radical scavenging assay, and ferric reducing antioxidant power assay (FRAP) of the BG-UAE extract were 54.00 ± 1.19 mg GAE/g sample, 291.47 ± 3.34 mg QE/g sample, 65.37 ± 1.78 mg TAE/g sample, 106.45 ± 1.21 mg TEAC/g sample, 163.58 ± 2.76 mg TEAC/g sample, and 121.31 ± 4.75 mg FeSO4/g sample, respectively. Except for FRAP, BG-UAE exhibited the highest values in all parameters compared to the other extraction methods. Catechins and caffeine were predominantly detected in tea flower extracts through UAE with BG and ethanol (EtOH-UAE). BG-UAE exhibited greater cell viability and cellular antioxidant activity than EtOH-UAE. The researcher expects that this research will contribute to the emergence of a green extraction technique that will offer larger functional components with economic and environmental benefits and minimal chemicals and energy use.

Metamorphic aerial robot capable of mid-air shape morphing for rapid perching

Aerial robots can perch onto structures at heights to reduce energy use or to remain firmly in place when interacting with their surroundings. Like how birds have wings to fly and legs to perch, these bio-inspired aerial robots use independent perching modules. However, modular design not only increases the weight of the robot but also its size, reducing the areas that the robot can access. To mitigate these problems, we take inspiration from gliding and tree-dwelling mammals such as sugar gliders and sloths. We noted how gliding mammals morph their whole limb to transit between flight and perch, and how sloths optimized their physiology to encourage energy-efficient perching. These insights are applied to design a quadrotor robot that transitions between morphologies to fly and perch with a single-direction tendon drive. The robot’s bi-stable arm is rigid in flight but will conform to its target in 0.97 s when perching, holding its grasp with minimal energy use. We achieved a 30% overall mass reduction by integrating this capability into a single body. The robot perches by a controlled descent or a free-falling drop to avoid turbulent aerodynamic effects. Our proposed design solution can fulfill the need for small perching robots in cluttered environments.

An Improved Lifetime and Energy Consumption with Enhanced Clustering in WSNs

A wireless sensor network (WSN) is made up of sensor nodes that communicate via radio waves in order to conduct sensing functions. In WSN, the location of the base station is critical. Although base stations are fixed, they may move in response to data received from sensor nodes under specific conditions. Clustering is a highly efficient approach of minimising energy use. The issues of extending the life of WSNs and optimising their energy consumption have been addressed in this paper. It has been established that integrating mobile sinks into wireless sensor networks extends their longevity. Thus, this research proposes an optimal clustering and routing technique for optimising the energy usage and lifetime of WSNs. To minimise energy consumption, this research employs movable and stationary sink nodes. The K-Medoid clustering model is used to generate the initial number of nodes in the various clusters. After that, the cluster head is chosen using a hybrid Interval Type-2 Fuzzy technique that takes three aspects into account: residual energy, node centrality, and neighbourhood. A highly efficient backup cluster head (CH) collecting system can provide in significant energy savings while also prolonging the system’s life. Finally, better Reinforcement learning combined with a Genetic algorithm routing protocol is used to ensure effective data delivery. The suggested approach’s efficacy is evaluated in comparison to earlier approaches utilising residual node energy, delay or average delay, packet delivery ratio, throughput, network longevity, average energy consumption, and multiple alive nodes. In experiments, the proposed strategy outperforms existing strategies.

Louver configuration comparison in three Canadian cities utilizing NSGA-II

Shading devices, particularly louvers, if chosen and designed correctly, reduce excessive uncontrolled solar gains and daylight in interior space, consequently improving building energy performance and occupants' visual comfort. However, various questions related to their adaptation to different climates, their designs, and optimized methods are unanswered. This paper aims to show that even in geographical areas with similar climate conditions, different louver designs are required.To this end, louver designs in three Canadian cities with almost similar climates are investigated. Objective functions are defined as minimizing energy use intensity and maximizing daylight use. Through a sensitivity analysis, louver parameters have been examined to determine which ones have a significant or insignificant effect on energy performance and visual comfort. Based on the results obtained from the sensitivity analysis, the proper range of each parameter was determined, and a metaheuristic algorithm was applied to reduce time and cost of optimization. Two algorithms (SPEA-2 and NSGA-II) are compared to understand which performs better in this problem. The results show that NSGA-II performs slightly better, and, using louvers in all case study cities positively affects visual comfort and energy performance. Second, louver depth and count are the most critical parameters in all three cities, and other parameters have a different effect based on the city latitude. The research contribution is to propose a workflow to integrate parametric design, sensitivity analysis, and NSGA-II to optimize louver design with various parameters and ranges. Also, the research proposes a guideline for designing louver in the three investigated cities.

Gas Cooled Graphite Moderated and Pressurized Water Reactor the Optimal Choice for Nuclear Power Plants Based on a New Group Decision-Making Technique

The aim of this work is to introduce the novel concept of an m-polar fuzzy soft set, including various types of algorithms and their fundamental operations. We created mathematical modeling to analyze operational rules and discuss the advantages, disadvantages, and natural aspects of algorithms for six types of nuclear power plants. It has been determined that emerging trends and the benefits of algorithms are increasing step by step. The suggested modeling with an m-polar fuzzy soft set is integrated into the fuzzy mean environment to analyze the effect of the correlation between decision factors and decision results without an excessive duty cycle, thus minimizing energy use and other adverse effects. Based on a new group decision-making technique considering an asymmetric weight vector, we proved that Gas Cooled, Graphite-Moderated, and Pressurized Water Reactors are the optimal choices for nuclear power plants. In the end, a numerical illustration is provided for selecting the best photo to demonstrate the use of the generated technique and to exhibit its adequacy.

Optimizing the Air Conditioning Layouts of an Indoor Built Environment: Towards the Energy and Environmental Benefits of a Clean Room

Reducing energy consumption in buildings has received intensified research impetus since the introduction of the decarbonization goals set in the Paris agreement. Many domestic and specialized applications require clean rooms (indoor built environments) for safe and clean operation. Energy efficiency in clean room spaces depends on maintaining livable or required conditions such as temperature, humidity, and particle concentration with minimal use of energy and new carbon dioxide (CO2) emissions. In the literature, parameters such as temperature, relative humidity, particle concentrations, and CO2 emissions are not able to be properly controlled in clean room systems. The designed system in the literature involves high energy consumption and high economic costs. All these factors add novelty to this research, which was a significant research gap in previous studies. This clean room is directly linked to environmental parameters such as ambient temperature, relative humidity, etc. The clean room is also related directly to the building and infrastructure in such a way that there are certain regulatory requirements for designing a clean room. For designing and constructing the controlled environment in a clean room, the English (EN) documents, ISO 9000, and various other standards allow for clean rooms for different types of products. In this research, the designed control configurations properly control the system. Additionally, this system is energy efficient, with positive environmental aspects regarding CO2 emissions. Three control configurations were designed in this research, option A, option B, and option C, and three parameters are controlled in the study. These parameters are room temperature, relative humidity, and CO2 emissions (outside the room). CO2 emissions are controlled outside the room (in the environment). In the last research phase, a comparative analysis of these three control configurations was performed to find an energy-efficient system with fewer CO2 emissions. Control configuration B (option B) provides reliable results regarding an energy-efficient system and fewer CO2 emissions emitted to the environment. In this study, an optimized configuration for the air conditioning system was developed for a clean room (volume 185.6 m3) with a required temperature of 23 °C, relative humidity of 40%, and a particle size of less than 0.3 μm. Three different design configurations were analyzed using TRNSYS simulation software. The minimization of energy use and CO2 emissions were the objective functions. Energy loads were calculated for each of the configurations by varying the fixed air change per hour and the minimum outdoor air flow rate. The results of a whole year simulation run for control configurations A, B, and show that, on the one hand, the ambient weather conditions of temperature and relative humidity (RH) is varied throughout year and, on the other hand, the clean room temperature was maintain at exactly 23 °C, which is the required set point temperature, for all the three configurations (A, B, and C). Furthermore, the clean room relative humidity was maintained at 36% for configuration A, below the 40% which was the set point for clean room relative humidity, and at 40% for configurations B and C. Configuration B exhibited the minimum energy use (7300 kWh), at a fixed air change per hour value of 20 and a minimum outdoor air flow rate of 150 L/s, with the least amount of CO2 emissions, offering an overall 25% improvement over configurations A and C.

Hydrothermal carbonization of cow dung with human urine as a solvent for hydrochar: An experimental and kinetic study

Hydrothermal carbonization (HTC) is the most cost-effective, environmentally friendly, and efficient physicochemical and biochemical process for converting biomass to products with added value. The objective and novelty of this work is to produce and investigate the qualities of hydrochar fuel (as a solid fuel) from cow manure using human urine as a solvent in order to find a suitable replacement for conventional fuel (i.e., coal). HTC based studies were conducted in batch, at three different reaction temperatures (180 °C, 200 °C, and 220 °C) and two different reaction periods (2 and 4 h). For kinetic analysis and reaction mechanism of the combustion behavior of the produced hydrochar, the model free kinetic methods and the z-master plot were used. From the model free kinetics methods, it was observed that the resultant optimum average activation energy and pre-exponential factor for the produced hydrochar at 180 °C and 2 h reaction period (HTC_180_2) were ∼120 kJ/mol and ∼5.59 × 1025 sec−1, respectively. In addition, the little variation between ΔEα and ΔHα (∼10 kJ/mol) suggests that the combustion of produced hydrochar (HTC_180_2) occurred with minimal energy use. Furthermore, the hydrochar exhibited its highest heating value at 200 °C for 4 h (HTC_200_4) which was 1.44 times higher than the raw dung (13.4 MJ/kg) due to the HTC process. The produced hydrochar demonstrated a significant improvement compared to the conventional solvent, i.e. water.

Production of Photovoltaic Electricity at Different Sites in Algeria

The necessity for further growth in the demand for electricity, particularly in the remote countryside, has been essential to Algeria’s economic and social progress in recent years. The Saharan regions account for around 80% of Algeria’s surface and have significant power demands. These areas are distinguished by their scattered population, extremely hot weather with high radiation levels (7 kw h/m2/day), and minimal energy use. Photovoltaic technology is one of the finest ways to harness solar power. The conversion of radiant energy (light quanta) into electrical energy can be achieved with the use of semiconductor materials. The effects of a solar cell depend on parameters such as solar cell technology, the position of the photovoltaic array, climate, and geographic data of the site. This paper presented: the role of site choice in the production of photovoltaic electricity. The results obtained for five sites in Algeria: Ouargla, Algers, Bechar, Sidi Bel Abbès, and Batna, confirmed that desert sites (Ouargla, Bechar) have a good photovoltaic efficiency result with large solar power irradiance.

Optimizing Transportation between Sea Ports and Regions by Road Transport and Rail and Inland Waterway Transport Means Including “Last Mile” Solutions

Optimization transportation cargo and passengers between ports and regions are very important, because industrial regions are located some distance from ports. The demand for energy request for the movement of transport is a necessity in the modern world. Transport and activity called transportation are used daily, everywhere, and a lot of energy is needed to power the various transport modes. Today different transport modes are being used to transport passengers and cargo. It is quite common to use road transport, which can transport passengers and cargo from door to door. Considering alternative possibilities (road, railway and/or inland waterway transport), it is important, based on theoretical and experimentation, to identify optimal solutions. In finding transport modes that are either most technically or economically effective, we could unearth possible solutions which would require minimal energy use. Unfortunately, with increased transportation, this often leads to traffic congestion on the roads, which requires additional energy (fuel). This situation generates requirements from many stakeholders in terms of finding ways to decrease the transportation time and energy (fuel) consumed by transport modes. A theoretical method evaluation is conducted on the optimal transportation possibility that minimizes transportation time and energy (fuel) use by employing graph theory, which is presented in this paper. The scientific contribution is the development of a transport modes comparative index, which is then used for evaluations. This paper presents possible alternative transportation conditions based on a multi-criteria evaluation system, proposes a theoretical basis for the optimal solutions from an eco-economic perspective that considers energy, and provides for experimental testing during a specific case study. The final results from the case study provide recommendations and conclusions.