Simulation-based Optimization Framework Research Articles

Components optimization of polyurethane-modified asphalt binder towards compatibility: Insight from molecular dynamics simulations

Polyurethane-modified asphalt demonstrated the enhanced durability and skid resistance, while remaining the drawback in the compatibility between the polyurethane modifier and asphalt binder in practical use. Herein, a molecular-dynamics-simulations based compatibility evaluation method is proposed for the polyurethane modifier and asphalt binder, thereby achieving the cost-effective and efficient components optimization. Twelve components asphalt molecular model was built in Molecular Dynamics to investigate the interaction of asphalt binder to the polypropylene glycol, toluene diisocyanate, and diphenylmethane diisocyanate, respectively. The molecular model of polyurethane modified asphalt binder was verified by the similar loss modulus temperature from the simulation and temperature scanning tests. The simulation outcomes indicate significant variations in the compatibility of different polyurethanes with asphalt, in which diphenylmethane diisocyanate-type polyurethane exhibits the superior compatibility. The compatibility of various polyurethane concentrations in asphalt shows considerable disparity, suggesting that the compatibility index can serve as a criterion for determining the optimal dosage of the modifier. Temperature also exerts an influence on the compatibility of polyurethane modified asphalt binder. Simulation results indicates that 130 °C potentially may be the optimal temperature for its preparation, which is corroborated by the microscopic characterization. This study provides a fundamental framework for the molecular dynamics simulation-based components optimization of polyurethane-modified asphalt, thereby inspiring the targeted design of asphalt materials.

Revealing electricity conversion mechanism of a cascade energy storage system

With the increasing penetration of renewable energy in the power system, it is necessary to develop large-scale and long-duration energy storage technologies. Deploying pump stations between adjacent cascade hydropower plants to form a cascade energy storage system (CESS) is a promising way to accommodate large-scale renewable energy sources, yet the mechanism how renewable curtailment is converted to hydroelectricity is still unclear. In this paper, we aim to clarify this mechanism by evaluating the CESS's long-term operational efficiency and changes compared to the cascade hydropower system. First, operational features and principle of the CESS was outlined. Then, long-term operations of the CESS and cascade hydropower system were, respectively, optimized using a simulation-based optimization framework. Finally, the operational efficiency of the CESS, defined as a ratio of reduced renewable energy curtailment to increased hydropower production, was calculated based on the above two optimized operation results. China's Longyangxia-Laxiwa CESS was selected as a case study. Results show that: (1) long-term operational efficiency of the CESS reached 81.6 %, i.e., 81.6 % of the curtailed renewable could be converted to hydroelectricity; (2) the increase in hydropower production was mainly attributed to the upper hydropower plant, accounting for about 96.5 % of the total increment; and (3) the increase in generation flow was the main factor to impact the increased hydropower production of the upper plant, although it could decrease the hydropower plant's hydraulic head.

Design and optimization of boil-off gas recondensation process by recovering waste cold energy in LNG regasification terminals

In any LNG regasification terminal, boil-off gas (BOG) generation is unavoidable. BOG being predominantly methane, a sustainable and cost-effective BOG management process for any given LNG terminal conditions is of utmost necessary from both economic and environmental perspectives. LNG has substantial amount of cold energy, which is completely wasted when LNG is regassified using air or water at ambient conditions. In this study, we have developed a superstructure encompassing various process integration options to recondense the generated BOG completely using LNG cold energy. The proposed superstructure is optimized using a simulation-based optimization framework to minimize the total annualized cost for the BOG management process. We have performed several case studies to investigate the impact of different LNG terminal conditions on the BOG management process. Results obtained from our proposed framework not only yields the optimal configuration for BOG recondensation process but also provides the optimal operating conditions for any given LNG terminal condition. Our results show that variations in LNG compositions and regasification pressures do not influence the optimal configuration but they do influence the optimal process operating conditions. The amount of BOG to be recondensed strongly influences both optimal BOG management configuration as well as the process operating conditions.

Dynamic joint decision of matching parameters and relocation strategies in ride-sourcing systems interacting with traffic congestion

As the ride-sourcing market expands, ride-sourcing fleets have increased urban traffic congestion, and in turn, road congestion is affecting ride-sourcing operations. It is crucial to incorporate this interaction mechanism into the operational decision of ride-sourcing systems. This mechanism can be described in detail by simulation models. Applying simulation-based optimization methods to the real-time operation of ride-sourcing systems is challenging due to the limitations of simulation efficiency. To tackle this issue, this study first proposes a modular simulation model combining time-driven and event-driven mechanisms. The simulation model comprises two layers: the traffic flow module at the bottom, defined by trip-based and multi-region macroscopic fundamental diagrams and cell transmission models, and the ride-sourcing operation module at the top, which considers the interaction between the traffic congestion and ride-sourcing operation and models the random behavior of passengers and drivers. We verify the efficiency and accuracy of the simulation model. The simulation model is utilized as an evaluator of the objective functions and performance measures. Then, we integrate the Bayesian optimization, parallel sampling, and rolling horizon approaches to develop an efficient and effective simulation-based optimization framework for the dynamic joint decision-making of matching radius, matching intervals, and threshold relocation strategies. Six groups of experiments for the traffic scenario in Berlin reveal some interesting findings. (1) The matching rate first increases and then decreases with the matching radius (a concave function), while the closest study does not think so. (2) As the fleet size grows, passenger waiting time reaches its minimum and then rises because of the increased endogenous congestion generated by ride-sourcing fleets. (3) The optimal operation strategy considering endogenous congestion not only leads to an increase in the system revenue and efficiency but also leads to congestion relief, even though the latter was not our initial objective. This benefit is the most pronounced when the fleet size and background traffic are moderate.

Bi-objective simulation optimization for online feedback control of variable speed limits considering uncertain traffic demands and compliance behaviors

Abstract Variable Speed Limits (VSL) stand out as a well-established and effective strategy to alleviate traffic congestion and enhance traffic safety on motorways. It allows Variable Message Signs (VMSs) to dynamically determine the speed limits according to real-time traffic states. This paper introduces an innovative online feedback control approach designed to regulate speed limit values on Variable Message Signs (VMSs), addressing multiple bottlenecks while considering their spatiotemporal constraints. Moreover, we offline optimize the gain coefficients of this feedback control approach in the simulation-based optimization (SBO) framework. Specifically, with average and variance of space-mean speeds as bi-objectives, a stochastic SBO model considering uncertain traffic demands and compliance behaviors is established and solved by a bi-objective surrogate-based promising area search (BOSPAS) algorithm. Real-field experiments conducted in Edmond City demonstrate the well-performing bi-objectives of the proposed approach, especially in handling uncertain compliance behaviors and traffic demands. Compared with the uncontrolled scenario, the feedback control schemes with the offline optimized gain coefficients improve the average and variance of space-mean speeds by up to 16.2% and 20.8%, respectively. Meanwhile, by the comparison of detailed performances, it is found that the optimized control schemes perform better than the uncontrolled scheme from the overall and local aspects. In conclusion, this study puts forward a general framework that applies an online feedback control approach with gain coefficients optimized offline by an SBO method to deal with real-time decision-making problems under uncertainties.

Joint Discount and Replenishment Parametric Policies for Perishable Products

We consider a joint discount and replenishment problem in a discrete periodic review fashion for the sale of a perishable product, characterized by limited deterministic shelf life, replenishment lead times, and stochastic demand. Customers decide what to buy according to a linear discrete choice model, balancing price and perceived quality, uniquely determined by the residual shelf life. The decisions we consider are: How many new items to order, the age of the items to be discounted, and how much discount to offer. In this context, we compare a set of policies mixing the constant order policy and the base stock one with some easy discounting policies, optimizing their parameters using a simulation-based optimization framework. To evaluate their performance in terms of revenue and quantity of scraped items, we consider four realistic instances for a grocery retailer characterized by products of different shelf life and variance of demand. Experiments show that best results are achieved by a base stock policy that discounts products of different ages based on a threshold: If the quantity of the inventory of a given age is greater than a threshold it applies a discount, otherwise no discount is proposed. In the presented configurations, this policy increases the average reward compared to policies that do not discount.

A Simulation-Based Multi-Objective Optimization Framework to Enhance Patient Satisfaction: A Case Study of Ophthalmology Department Management

A Simulation-Based Multi-Objective Optimization Framework to Enhance Patient Satisfaction: A Case Study of Ophthalmology Department Management

A black-box optimization strategy for customizable global elastic deformation behavior of unit cell-based tri-anti-chiral metamaterials

Metamaterials are a class of materials with a distinctive unit cell-based periodic architecture, often resulting in unique mechanical properties. The potential of metamaterials can be further improved by using gradients of unit cell parameters and thereby creating a specific distribution of material properties in a part. This design freedom comes with the challenge of finding new suitable design and optimization strategies. In this study, a Finite Element simulation-based optimization framework to create a predefined deformation behavior of unit cell-based metamaterials is presented. The framework consists of a numerical homogenization method to create an efficient linear elastic material representation, an interpolation scheme for a fast correlation of unit cell parameters with homogenized material properties and a black-box based optimization part. The framework is tested on a tri-anti-chiral metamaterial with additional geometric parameters and a newly developed transition unit cell. Several specified lateral deformations of simple 2D rectangular test specimens under tensile load are used as primary optimization targets and the homogenized results are validated against Finite Element simulations of fully modeled tri-anti-chiral structures. In addition, the effect of the design space and the number of design variables is studied and several combinations of optimization objectives and constraints are tested.

Multi-dimensional management framework on fresh groundwater lens of Kish Island in the Persian Gulf, Iran

Groundwater in arid and semi-arid coastal aquifers is vulnerable to seawater intrusion and quality deterioration despite being one of the most reliable sources of water supply due to the increasing number of development plans and competition between water consumers. A multi-dimensional groundwater management framework is developed to trade-off between groundwater abstraction, allocation equity, groundwater quality, and energy considerations in the reverse osmosis (RO) filtration process in the fresh groundwater lens of Kish Island, Iran. An arid island confined in the Persian Gulf is modeled using 3D simulation and three well-occupied multi-objective evolutionary optimization algorithms. Four objectives include: maximizing the groundwater abstraction, minimizing the Gini coefficient (allocation inequity), minimizing the total energy required to pass saline water through the RO membrane to reach the standard total dissolved solids (TDS), and minimizing the average TDS concentration of water abstraction positions from 11 management zones have been considered over a 50-year management horizon. Solutions obtained in the simulation-based constrained multi-objective optimization framework allow managers to choose from 587 Pareto optimal solutions. They provide an abstraction scheme with a range of 1.44 to 4.53 MCM/yr, a Gini coefficient of 0 to 0.98, filtration energy of 988,562 to 1,935,760 kWh/yr, and an average TDS of 19,663 to 21,351 mg/L. The Pareto optimal solutions can help decision-makers decide on the multi-dimensional problems of sustainable coastal groundwater management and show patterns among different objectives.

Techno-economic assessment of H2 extraction from natural gas distribution grids: A novel simulation-based optimization framework for pressure swing adsorption processes

Green H2 plays an important role in the global energy transition. To improve its supply chain economics, the already existing gas grids can be an interim solution for its storage and transportation infrastructure. Injecting H2 into the gas networks seems straightforward from a process design standpoint. However, the extraction, at the other end of the supply chain, poses challenges, and its feasibility from the techno-economic viewpoint is crucial and can support the further development of such programs. This article presents a rigorous techno-economic and optimization model for this process developed in Aspen Adsorption V11 and MATLAB. This holistic model uses the cyclic steady-state approach of Aspen Adsorption V11, which avoids the redundant computations for achieving the steady-state performance in the dynamic simulation approach and allows for the development of a novel simulation-based optimization framework. The proposed optimization model can be used in other pressure swing adsorption applications to improve the rigorousness of the optimal solution and computational time. The results show that H2 extraction from a distribution grid with 30 mol% H2 and 70 mol% CH4 at 12 bar operating pressure costs $0.8064/kg-H2 and $0.9012/kg-H2 for product purities of 99.00 mol% and 99.97 mol%, respectively. Additionally, this study explores the impact of N2 and CO2 presence in natural gas on H2 separation costs. For natural gas containing CH4: CO2: N2 with a molar ratio of 95:2:3 mixed with 30 mol% H2, the separation cost of the 99.00 mol% pure H2 increases by 11.6% to $0.8998/kg-H2; however, based on the simulation, the purity of 99.97 mol% due to the presence of N2 is not technically achievable.

Deriving operating rules for inter-basin water transfer projects incorporating a scenario reduction strategy

Streamflow uncertainty is one of the main challenges in managing reservoir systems. To accurately characterize the streamflow uncertainty in a reservoir operation model, numerous streamflow scenarios should be considered, but this results in a huge computational burden for multi-objective optimization. Here, we propose a methodology for deriving the operating rules for an inter-basin water transfer project (IBWT). The methodology adopts a scenario-reduction strategy to balance accuracy against the computational burden. The proposed method first reduces a large number of streamflow scenarios to several typical scenarios and the corresponding occurrence probabilities using a simultaneous backward reduction method. A multi-objective optimal operation model is then formulated to optimize the expected values of performance indices of the IBWT system under the typical scenarios. Finally, a simulation-based optimization framework is adopted to derive the multi-objective operating rules. To test the method, the Han to Wei IBWT project in northwest China is considered as a case study. The results show that the proposed methodology produces effective reservoir operating rules with a relatively low computational burden. After the initial 1,000 streamflow scenarios have been reduced to 20, the model solution time decreases significantly, from 49.17 h to 1.05 h, while the changes in mean value (0.47%) and standard deviation (0.93%) of these scenarios are less than 1%. Compared with an operation scheme that uses only historical streamflow data, the operation performance is further improved in terms of energy production, energy consumption, and water shortage index when the streamflow uncertainty is considered.

Optimized design and integration of energy storage in Solar-Assisted Ground-Source Heat Pump systems

The integrated use of multiple renewable energy sources to increase the efficiency of heat pump systems, such as in Solar Assisted Geothermal Heat Pumps (SAGHP), may lead to significant benefits in terms of increased efficiency and overall system performance especially in extreme climate contexts, but requires careful integrated optimization of the different system components. In particular, thermal storages take a fundamental role in optimizing the integration of renewable energy sources and the system operation. This work investigates the potential design optimization of a SAGHP system in a mountain site by exploring many different alternatives to optimize the mutual relationship between the solar field, the geothermal field and the water thermal storages. This is done through an original simulation-based multi-objective optimization framework considering energy efficiency and economic feasibility, which allows appraising the impact of the different design alternatives on the overall system performance and on the dynamics of the different system components. Results identify a set of optimized system configurations that optimize the integrated exploitation of the different thermal sources showing a potential increase of the overall system performance leading to 34% lower global cost compared to the initial design. High robustness of the optimal design solutions is reported with respect to the current context of high economic uncertainty.

Simulation-based multi-objective optimization framework for sustainable management of coastal aquifers in semi-arid regions

Groundwater is a strategic source of water supply, especially in arid and semi-arid coastal regions. Growing demand, along with scarce water sources, may impose intense pressure on this precious resource. This pressure will degrade water quality for future use and cause social inequality, despite supplying current needs. A novel sustainable management model for water allocation is developed to address these interconnected concerns in coastal aquifers. Three aspects of sustainable development are considered: groundwater quality with total dissolved solids (TDS) indicator for the environmental part, gross value added from water for the economic efficiency, and the Gini coefficient for social inclusion and equity. The problem is solved with a simulation-based multi-objective optimization framework using a numerical variable-density simulation code and three approved evolutionary algorithms, NSGA-II, NRGA, and MOPSO. The obtained solutions are integrated to enhance the solutions' quality by using each algorithm's strengths and dominated members' elimination. In addition, the optimization algorithms are compared. The results showed that NSGA-II is the best in terms of solutions quality, with the least number of total dominated members (20.43%) and a 95% success rate of obtained Pareto front. NRGA was supreme in finding extreme solutions, the least computational time, and diversity, with an 11.6% higher diversity value than the second competitive NSGA-II. MOPSO was the best in spacing quality indicator, followed by NSGA-II, showing their great arrangement and evenness in obtained solution space. MOPSO has the propensity for premature convergence and needs more stringent stopping criteria. The method is applied to a hypothetical aquifer. Still, the obtained Pareto fronts are determined to assist decision-makers in real-world coastal sustainable management problems by illustrating existing patterns among different objectives.

Agent-based simulation and optimization of hybrid flow shop considering multi-skilled workers and fatigue factors

In the past few decades, more and more studies have begun to consider the impact of human factors on manufacturing systems. This paper studies a hybrid flow shop scheduling problem considering multi-skilled workers and fatigue factors. An agent-based simulation system is established to cope with the uncertainties in the worker fatigue model. Furthermore, this paper proposes a novel simulation-based optimization (SBO) framework, which combines genetic algorithm (GA) and reinforcement learning (RL) to address the hybrid flow shop scheduling problem. Numerical experiments are conducted on several instances with different production configurations. In particular, a pharmaceutical production facility is modeled as a hybrid flow shop to demonstrate the feasibility and effectiveness of the proposed SBO method.

Improving ship sustainability by re-using engineering simulators in multi-objective optimization

Alternatives for enhancing a cruise ship’s energy efficiency were investigated by introducing different waste heat recovery technologies and battery systems in the machinery. A ship designer’s in-house ship energy system simulator was applied in a cloud-based framework for simulation-based optimization. This multi-objective optimization with economic and environmental objectives used genetic algorithm for finding the best overall solution in a complex ship energy system design task. The results suggest that adding battery capacity alone contributes very moderately to reducing the case ship fuel consumption and, therefore, carbon emissions. Nevertheless, a combination of steam turbines and organic Rankine cycle units would offer the largest fuel saving potential with the lowest investment cost in the case setup. Also, the main engines’ running hours were of interest. The presented approach can bring significant added value for sustainable ship design with minimal additional effort.

Simulation-based optimization framework to increase distribution system photovoltaic hosting capacity through optimal settings of smart inverter Volt-VAr control function

Simulation-based optimization framework to increase distribution system photovoltaic hosting capacity through optimal settings of smart inverter Volt-VAr control function

Adaptive machine learning-based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery

In this contribution, we develop an efficient surrogate modeling framework for simulation-based optimization of enhanced oil recovery, where we particularly focus on polymer flooding. The computational approach is based on an adaptive training procedure of a neural network that directly approximates an input-output map of the underlying PDE-constrained optimization problem. The training process thereby focuses on the construction of an accurate surrogate model solely related to the optimization path of an outer iterative optimization loop. True evaluations of the objective function are used to finally obtain certified results. Numerical experiments are given to evaluate the accuracy and efficiency of the approach for a heterogeneous five-spot benchmark problem.

Design of a biomass-heating network with an integrated heat pump: A simulation-based multi-objective optimization framework

The integration of compression heat pumps is a promising technology to recover waste heat from exhaust gases of a biomass-based heating network. This requires, however, the consumption of additional electricity, which has a high emission factor or a high price in many countries. In this context, the integration of small-scale gasifier cogeneration is supposed to be superior technology to provide the necessary power for heat pumps. Nevertheless, the multiple possibilities of integrating these components imply a high degree of system complexity and, therefore, higher design requirements. To maximize the benefits of the integrated system, development of an optimization approach at the system level is necessary, so that the connection variants of the heat pump, the installation of gasifier cogeneration, and their optimal design can be adequately planned. This work introduces a multi-objective simulation–optimization framework for the design of the proposed integrated system based on the genetic algorithm, taking into account the complex thermodynamic processes as well as the techno-economic performances and environmental impacts of the concepts. As a case study, an existing biomass heat network located in Germany is investigated to test the capabilities of the proposed approach. The analysis of the optimization results demonstrates that it is possible to ensure the effective utilization of biomass resources while simultaneously achieving the economic and environmental compatibility of the system through an appropriate optimization design. The proposed simulation–optimization framework allows decision-makers to achieve an optimal system design under the given constraints and the chosen objectives.

CFD-based multi-objective optimization of the duct for a rim-driven thruster

Rim-driven thruster (RDT) is a new integrated thruster that has attracted much attention recently. The RDT provides the benefits of reducing vibration and noise and increasing useable volume by mounting the propulsion motor stator into the duct and fusing the motor rotor with the propeller blades. The calculation method of RDT considering the motor gap was discussed and validated. A simulation-based design optimization framework was proposed for the particularity of RDT ducts. The parametric reconstruction of the duct was executed based on the Bézier curve. The optimal Latin hypercube design method was used to generate samples, and the approximate model was developed based on the radial basis function (RBF) neural network. The fitting R2 of efficiency, minimum pressure, blade thrust, and total thrust of the approximate model reached 0.998, 0.955, 0.998, and 0.990, respectively. On this basis, the NSGA-II was employed to optimize the duct with minimum pressure and efficiency as the optimization objectives. Sensitivity analyses indicate that the optimization objectives are significantly affected by the nozzle size, followed by the internal nozzle profile. The typical Pareto solutions were verified by CFD calculation, and the duct optimization can improve efficiency by 3.3% or minimum pressure by 5.3%.

시뮬레이션 기반의 최적화를 이용한 제조 공장의 디지털 트윈 프레임워크 개발 - 원유정제 공장의 실시간 운전 최적화 적용

A digital twin framework with Simulation-based Optimization (SBO) for industrial plants is developed. The proposed framework is composed of a combination of DWSIM and Python. DWSIM is adopted for SBO simulation because it is open source process simulation software, used to model and simulate rigorously the processes such as refinery, petrochemical, chemical, power generation and utilities. Python is adopted for SBO optimization because it provides free optimization libraries for solving various forms of nonlinear optimization problems. The combination of Aspen HYSYS and MATLAB was previously proposed for SBO and used primarily for process synthesis and design because of its advanced simulation and optimization capabilities. This study applied the SBO framework with DWSIM and Python to Crude Unit real-time optimization (RTO). The result showed that the time requirement of RTO configuration, calculation efficiency and convergence stability are acceptable compared with RTO solutions already commercialized, confirming that the SBO framework with the combination of DWSIM and Python can be helpful for Digital Twin development in industrial plants.