A mixed-mode traffic assignment model with new time-flow impedance function

ABSTRACTThe rapid growth of interconnected devices and data traffic necessitates robust mobile networks, especially in rural areas with unreliable grid power. This paper introduces an optimisation framework for the off‐grid green open radio access network architecture utilising renewable energy to meet this demand. The framework aims to optimise power and bandwidth allocation, ensuring high data rates and reliability from sustainable sources. In addition, the CO2 emission factor and the quality of service are also optimised in which several assumptions and network metrics have been incorporated such as power consumption (PC), load demand, number of virtual machines (VMs) and maintenance cost. The problem is formulated as the weighted nonlinear problem. First, the model has been formulated using simplified data rate assumption and has been solved mathematically using Lagrange multipliers. Subsequently, the mathematical problem has been solved using the gradient descent numerical solution and then compared with sequential quadratic programming (SQP) and interior point algorithms. The second assumption is formulated as the data rate considering the noise and interference factors; the comparative analysis of SQP, active set and interior point algorithms has been presented. The third case assumed the dynamic behaviour of the network of users and VMs, where the problem is also solved mathematically and numerically. Empirical evidence from rural deployments confirms the enhancement of mobile coverage and service provision through the integration of solar and wind‐powered base stations.

This paper discusses a kind of optimization problem with linear complementarity constraints, and presents a sequential quadratic programming (SQP) algorithm for solving a stationary point of the problem. The algorithm is a modification of the SQP algorithm proposed by Fukushima et al. [Computational Optimization and Applications, 10 (1998), 5-34], and is based on a reformulation of complementarity condition as a system of linear equations. At each iteration, one quadratic programming and one system of equations needs to be solved, and a curve search is used to yield the step size. Under some appropriate assumptions, including the lower-level strict complementarity, but without the upper-level strict complementarity for the inequality constraints, the algorithm is proved to possess strong convergence and superlinear convergence. Some preliminary numerical results are reported.

Multidisciplinary design optimization (MDO) was performed on a helicopter rotor blade. The blade was modeled as a rigid flapping blade for dynamics; Blade Element Theory (BET) was the analysis approach to model the aerodynamic loading, and a simple linearly elastic hollowed rectangular section was the main structural component. MATLAB was used to solve the flapping differential equations and its Sequential Quadratic Programming (SQP) and Genetic Algorithm (GA) were used for the optimization. A Particle Swarm Optimization (PSO) routine was also tested. The optimization process consisted of three cases. The first case was a simple cantilever beam under centrifugal and an assumed bending loads. The optimization was performed using the SQP, GA, and PSO algorithms. The SQP resulted in the superior design with 75.45 compared to the GA's 87.1 and the PSO's 79.2, but a local minimum was present. The second case was an expansion of the first case by turning it into multidisciplinary problem. Aerodynamics was included in the design variables and objective function. Only the SQP algorithm was used and there was a reduction in hub vertical shear by 33.6%. The blade mass increased by 36.84%. The last case was an improvement to the second by creating a multiobjective problem by including the hub radial shear and the results were improved significantly by reducing the hub vertical shear by 34.06% and radial shear by 17.87% with a reduction of blade mass by 23.86%.

Optimal Design of Heat Exchanger Networks by Using SQP Algorithm Based on GPU Acceleration

Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed co-gasification of biomass wastes from oil palm

PurposeThis study compares the environmental impacts of transitioning from a business-as-usual (BaU) internal combustion engine vehicles (ICEVs) pathway to one adopting battery electric vehicles (BEVs) in Qatar from 2022 to 2050. The analysis is based on geographically representative empirical data, focusing exclusively on the light-duty, personal vehicle sector. The research explores environmental performance trends, uncertainties, and potential implications of transitioning from ICEVs to BEVs within the Qatar National Vision (QNV) 2030 framework.MethodsUtilising the ReCiPe method, this time-dynamic life cycle assessment (LCA) assessed a range of relevant environmental impact categories: global warming potential, particulate matter, human toxicity, acidification and resource depletion. This analysis incorporates different light-duty vehicle (LDV) types such as sedans, sport utility vehicle (SUVs) and sport vehicles. The impacts of potential technological advancements, such as in fuel efficiency for ICEVs and charging electricity supply and/or battery technology for the BEVs, were included to provide a more encompassing view of the environmental implications of both vehicle types.Results and discussionDecreasing environmental impact for ICEVs and BEVs is observed, with BEVs’ greater potential in reducing Qatar’s transport sector’s carbon footprint. Uncertainties emerged as this potential decrease was not seen in all impact categories, nor vehicle technology or timeframe. This stresses the BEV’s transition importance of production location and energy sources. This was observed for the carbon footprint and overarching environmental impact of battery production, exacerbated in regions reliant on fossil fuel electricity. Qatar, endowed with substantial fossil fuel reserves, relies on natural gas for electricity provision; therefore, the potential benefits of introducing BEVs are limited without strong shifts to renewables. Further research in vehicle production, disposal and technological advancements will prove essential, especially in a maturing sector like electric vehicle production and processing.ConclusionsBEVs have the potential to reduce the environmental impacts of Qatar’s transport sector. Yet, the short payback period for newer BEVs is linked with the greenhouse gas intensity of electricity production, emphasising the dual challenge for Qatar with its reliance on fossil fuels. Considering environmental, economic and societal facets, a transition taking into account all facets of sustainability and not purely the introduction of BEVs is imperative in aligning with Qatar’s 2030 sustainable vision.RecommendationsA clear understanding of the socio-economic and environmental aspects of the ICEV-BEV transition is urgently required, emphasising production, disposal and technological innovations. Exploring alternative batteries and recycling methods can offer pathways to mitigate environmental concerns associated with BEVs. Regions like Qatar are underrepresented in the available literature, yet should be part of the research on sustainable transitions to provide insights on the opportunity and co-benefits that arise from the development of relevant sustainability transition planning.

With the rapidly increasing number of electric vehicle users, in many urbans transport networks, there are mixed traffic flows (i.e., electric vehicles and gasoline vehicles). However, limited by driving ranges and long battery recharging, the battery electric vehicle (BEV) drivers’ route choice behaviors are inevitably affected. This paper assumes that in a transportation network, when BEV drivers are traveling between their original location and destinations, they tend to select the path with the minimal driving times and recharging time, and ensure that the remaining charge is not less than their battery safety margin. In contrast, gasoline vehicle drivers tend to select the path with the minimal driving time. Thus, by considering BEV drivers’ battery management strategies, e.g., battery safety margins and en-route recharging behaviors, this paper developed a mixed user equilibrium model to describe the resulting network equilibrium flow distributions. Finally, a numerical example is presented to demonstrate the mixed user equilibrium model. The results show that BEV drivers’ en-route recharging choice behaviors are significantly influenced by their battery safety margins, and under the equilibrium, the travel routes selected by some BEV drivers may not be optimal, but the total travel time may be more optimal.

Comparison of SQP and AL algorithms for deterministic constrained production optimization of hydrocarbon reservoirs

Today, different measures are investigated to solve the challenge of a sustainable urban freight transport. Among them, electric vehicles are often viewed as an interesting solution but the purchase cost of these vehicles is commonly seen as a barrier to their adoption. However the different cost structure between electric and conventional vehicles makes an analysis of every cost particularly needed if fleet managers want to assess the real competitiveness of the vehicles. As a result, we developed a total cost of ownership model to assess the competitiveness of light commercial vehicles in the Brussels-Capital Region. This paper presents the results of the total cost of ownership analysis on 8 battery electric vehicles, 5 diesel vehicles and 2 petrol vehicles. The results of a sensitivity analysis of the model are also presented. The electric vehicles were found to be competitive with conventional vehicles in the category of the quadricycles and the light commercial vehicles with a payload lower than 1,000kg: five out of six battery electric vehicles had a cost lower than the conventional vehicles of their category. The situation is inverted for the battery electric vehicles with a payload above 1,000kg where the costs are always higher than the conventional vehicles because of the expensive purchase and battery costs. Since battery electric vehicles are found to be a viable solution for some parts of the logistics, the next challenge should be to convince the fleet managers of the benefits of battery electric vehicles.

Decarbonizing heavy-duty vehicles is becoming increasingly important, yet multiple renewable-based decarbonization pathways have not been compared systematically. To fill this gap, this study develops a techno-economic and environmental model that integrates the fuel cycle and vehicle cycle to assess these pathways for China. The model includes two energy storage technologies: batteries and hydrogen, three energy transmission options, and two vehicle types: fuel cell electric vehicles and battery electric vehicles. Five distinct low-carbon pathways are evaluated on a ton-kilometer basis, including cost, greenhouse gas emissions, and abatement cost relative to conventional diesel trucks. Results indicate that the most cost-effective battery electric vehicle pathway offers a 7 % lower ton-km cost than diesel trucks for a 49-ton gross vehicle weight. Battery electric vehicle charging combined with hydrogen for power generation is more cost-effective than direct use of hydrogen in fuel cell electric vehicles. All studied pathways can achieve over 80 % emission reduction compared to diesel trucks, with fuel cell electric vehicle pathways having the highest abatement costs of 103–117 USD/tonCO2. Analysis of the impact of gross vehicle weight and vehicle range indicates that battery electric vehicle pathways outperform fuel cell electric vehicle pathways across most dimensions. Costs of battery electric vehicle pathways do increase sharply for vehicle ranges beyond about 700 km, and fuel cell electric vehicles become more cost-effective relative to battery electric vehicles for ranges above approximately 1100 km, although all pathways are much more costly than diesel trucks at long ranges. The sensitivity analysis highlights the importance of powertrain efficiency and cost projections show that fuel cell electric heavy-duty vehicles can achieve cost-parity before 2030. These results highlight the desirability of promoting the adoption of heavier battery electric vehicles and providing more targeted subsidies for long-range fuel cell electric vehicles.

The present work proposes a methodology for optimization of a liquefied biomethane (LBM) production plant. The LBM production plant comprises amine-based absorption upgrading followed by a single expander refrigeration cycle. The processes were modeled using Aspen HYSYS® and optimized through a Sequential Quadratic Programming algorithm. Any changes in the operating conditions of the upgrading process will affect the cooling demand in the liquefaction, while the opposite is not true. Based on this, a sequential optimization approach starting with the upgrading process is proposed. In order to accommodate the connection between the processes, different objective functions were formulated for the sequential optimization approach. The results from the sequential approach were compared with an overall optimization approach, where the entire LBM plant was optimized simultaneously. The results indicate that the same solution was obtained both for the sequential approach and the simultaneous approach. For the sequential approach, however, the best result was observed when the interaction between the upgrading and liquefaction processes was accounted for by considering the effect of the upgrading process on the exergy requirement in the liquefaction process.

Location of public charging stations, range limit, and long battery-charging time inevitably affect drivers’ path choice behavior and equilibrium flows of battery electric vehicles (BEVs) in a transportation network. This study investigates the effect of the location of BEVs public charging facilities on a network with mixed conventional gasoline vehicles (GVs) and BEVs. These two types of vehicles are distinguished from each other in terms of travel cost composition and distance limit. A bilevel model is developed to address this problem. In the upper level, the objective is to maximize coverage of BEV flows by locating a given number of charging stations on road segments considering budget constraints. A mixed-integer nonlinear program is proposed to formulate this model. A simple equilibrium-based heuristic algorithm is developed to obtain the solution. Finally, two numerical tests are presented to demonstrate applicability of the proposed model and feasibility and effectiveness of the solution algorithm. The results demonstrate that the equilibrium traffic flows are affected by charging speed, range limit, and charging facilities’ utility and that BEV drivers incline to choose the route with charging stations and less charging time.

A life-cycle assessment of battery electric and internal combustion engine vehicles: A case in Hebei Province, China

In this work the effect of weight reduction using advanced lightweight materials on the mass, energy use, and cost of conventional and battery electric passenger vehicles is compared. Analytic vehicle simulation is coupled with cost assessment to find the optimal degree of weight reduction minimizing manufacturing and total costs. The results show a strong secondary weight and cost saving potential for the battery electric vehicles, but a higher sensitivity of vehicle energy use to mass reduction for the conventional vehicle. Generally, light weighting has the potential to lower vehicle costs, however, the results are very sensitive to parameters affecting lifetime fuel costs for conventional and battery costs for electric vehicles. Based on current technology cost estimates it is shown that the optimal amount of primary mass reduction minimizing total costs is similar for conventional and electric vehicles and ranges from 22% to 39%, depending on vehicle range and overall use patterns. The difference between the optimal solutions minimizing manufacturing versus total costs is higher for conventional than battery electric vehicles.

When applying the sequential quadratic programming (SQP) algorithm to topology optimization, using the quasi-Newton methods or calculating the Hessian matrix directly will result in a considerable amount of calculation, making it computationally infeasible when the number of optimization variables is large. To solve the above problems, this paper creatively proposes a method for calculating the approximate Hessian matrix for structural topology optimization with minimum compliance problems. Then, the second-order Taylor expansion transforms the original problem into a series of separable and easy-to-solve convex quadratic programming (QP) subproblems. Finally, the quadratic programming optimality criteria (QPOC) method and the QP solver of MATLAB are used to solve the subproblems. Compared with other sequential quadratic programming methods, the advantage of the proposed method is that the Hessian matrix is diagonally positive definite and its calculation is simple. Numerical experiments on an MBB beam and cantilever beam verify the feasibility and efficiency of the proposed method.