Simultaneous Optimization Research Articles

A Quasi-Newton Trust-Region Method for Well Location Optimization Under Uncertainty

Summary Subsurface development involves well-placement decisions considering the highly uncertain understanding of the reservoir in the subsurface. The simultaneous optimization of a large number of well locations is a challenging problem. Traditional gradient-based methods can be adapted for well location optimization (WLO) when these problems are converted into real-valued representations and equipped with protocols to handle noisy objective functions. However, their application to large-scale scenarios often remains impractical. This impracticality arises because computing gradients of the objective function can be prohibitively expensive in realistic settings, particularly without using the adjoint method. In this paper, we explore the application of a novel quasi-Newton trust-region (TR) method that employs the stochastic simplex approximate gradient (StoSAG). We have implemented the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton updating algorithm together with line-search (LS) and TR optimization strategies. The StoSAG-based optimization methods have been applied to a realistic synthetic reservoir featuring 26 wells considering two distinct cases: Each includes 20 realizations of porosity and permeability. The first case exhibits mild heterogeneity, while the second exhibits significant heterogeneity with a large correlation length. We have conducted a series of runs to evaluate the performance of these algorithms in addition to comparisons to the finite-difference (FD) and particle-swarm-optimization (PSO) algorithms. We introduce a novel approach to enhance the accuracy of StoSAG gradients by proposing modified StoSAG formulations. These formulations are tailored to exploit the structure of the objective function and to capture the relationships between its components and the individual optimization parameters. This approach involves using a correction matrix W informed by problem-specific knowledge. The entries of W vary from 0 to 1 and are proportional to the interference effects the neighboring wells have on each other. We determine those entries (or weights) based on the radii of investigation around the wells and the distance between the well pairs. Results indicate that the steepest-descent (SD) algorithm coupled with StoSAG has superior performance to PSO and FD. Although the objective function is prone to numerical noise and not continuously differentiable with respect to well locations, StoSAG overcomes this challenge because it acts as a smooth approximation. Comparative tests further confirm that the TR-BFGS is more effective than the LS-BFGS. Moreover, we show that using the proposed gradient correction procedure results in a significant acceleration in convergence, indicating an enhancement in the StoSAG gradient approximation quality. This enhancement allows the TR-BFGS algorithm to achieve considerably higher performance than SD, illustrating that the accuracy of the BFGS approximation benefits from improved gradient quality.

An application of simultaneous stochastic optimisation on an open-pit copper mining complex with supply, recovery, and market uncertainties

ABSTRACT A mining complex is an integrated value chain that consists of multiple mines, stockpiles, processors, waste dumps, and tailing facilities, and where the in-situ materials mined are transformed into products sold to customers. Simultaneous stochastic optimisation provides a strategic mine planning framework that incorporates all components of a mining complex into a single mathematical representation, capitalising on the synergies between components while considering uncertainty. An application of this optimisation framework at a coper mining complex demonstrates that the incorporation of material supply, market demand, and processing recovery uncertainties into the modelling leads to production schedules that yield a higher NPV and metal recovery.

Self-assembled chromium-based nitrogen carrier for chemical looping ammonia synthesis

Industrially, ammonia is generated mainly using the energy-intensive and CO2-emitting Haber-Bosch process, which is not conducive to sustainable economic and social development. The chemical looping ammonia synthesis (CLAS) is capable of coupling green energy, which is one of the available alternatives to the Haber-Bosch process, but the ammonia production rate of most of its nitrogen carriers is unsatisfactory. In this work, we designed a self-assembled nitrogen carrier using ZSM-5 molecular sieve (ZSM-5) and nickel (Ni) to modify chromium nitride (CrN). ZSM-5 molecular sieves have a rich pore structure, which enables the gas-phase reactants to react with the lattice nitrogen in the subsurface layer of the nitrogen carrier. Nickel can facilitate the dissociation of hydrogen during hydrogenation due to its higher hydrogen affinity. The ammonia production rate of the nitrogen carrier can reach 451.2 μmol g−1 h−1 at 700 °C and atmospheric pressure, and be able to remain 358 μmol g−1 h−1 after eight cycles. Furthermore, we also propose a full-coverage design strategy for nitrogen carriers: simultaneous optimization of the chemical and mass transfer processes in the nitrogen carrier. This work advances the design of high-performance nitrogen carrier, thus facilitating the applications of CLAS for the distributed ammonia synthesis.

Advanced energy management system with road gradient consideration for fuel cell hybrid electric vehicles

Hybrid electric fuel cell vehicles (FCHEVs) represent a promising pathway for sustainable transportation by integrating fuel cells, batteries, and supercapacitors to power their propulsion systems. However, ensuring the efficiency and longevity of FCHEV components remains a significant challenge due to stress on batteries and fuel cells, as well as fluctuations in the DC bus voltage. This paper presents an innovative approach to enhance the effectiveness and lifespan of FCHEV storage elements through an energy management system (EMS) based on a multi-objective genetic optimization algorithm that accounts for road gradient variations. The proposed methodology dynamically allocates energy among the fuel cell, battery, and supercapacitor, optimizing three key objectives simultaneously: minimizing fluctuations in fuel cell and battery power, reducing variations in DC bus voltage, and balancing performance, efficiency, and storage system longevity. Extensive simulations demonstrate that simultaneous optimization of these three objectives yields the best results. Under the NEDC cycle, the optimized EMS resulted in a 12 % increase in fuel cell stress but a significant 73 % decrease in battery stress, along with a 39 % reduction in DC bus voltage variations. For the UDDS cycle, there was a 37 % decrease in fuel cell stress, a 69 % decrease in battery stress, and a 29 % reduction in DC bus voltage variations. During the HWFET cycle, the optimized EMS achieved an 8 % decrease in fuel cell stress, a 49 % decrease in battery stress, and an 8 % reduction in DC bus voltage variations. By incorporating road gradient considerations, the EMS aims to maintain the battery state of charge and hydrogen consumption within acceptable ranges while enhancing FCHEV efficiency under various road gradient conditions. This study advances FCHEV technology, offering insights into the design and operation of more sustainable and efficient transportation solutions.

Crashworthiness Performance and Multi-Objective Optimization of Bi-Directional Corrugated Tubes under Quasi-Static Axial Crushing.

Longitudinal corrugated tubes (LCTs) exhibit stable platform force under axial compression but have low specific energy absorption. Conversely, circumferential corrugated tubes (CCTs) offer higher specific energy absorption but with unstable platform force. To overcome these limitations, this paper introduces a novel bi-directional corrugated tube (BCT) that amalgamates the strengths of both the CCT and LCT while mitigating their weaknesses. The BCT is formed by rolling a bi-directional corrugated structure into a circular tubular form. Numerical simulations of the BCT closely align with experimental results. The study further examines the influence of discrete parameters on the BCT's performance through simulations and identifies the tube's optimal design using the integral entropy TOPSIS method. A full factorial experimental approach is then employed to investigate the impact of radial amplitude, axial amplitude, and neutral surface diameter on the crushing behavior of the BCT, comparing it with the CCT and LCT. The results reveal that increasing Ai enhances the axial resistance of the structure, while increasing Aj reduces the buckling effect, resulting in a higher specific energy absorption and lower ultimate load capacity for the BCT compared to the CCT and LCT. A simultaneous multi-objective optimization of the CCT, LCT, and BCT confirms that the BCT offers superior specific energy absorption and ultimate load capacity. The optimal configuration parameters for the BCT have been determined, providing significant insights for practical applications in crashworthiness engineering.

Design of unreliable flow lines: How to jointly allocate buffer space and spare parts

The design of flow lines is an extensive area of research whereby most publications focus on buffer allocation to cope with random influences like breakdowns or random differences in processing times. In addition, spare parts can directly increase the machines’ availability. However, only a few papers consider the simultaneous optimization of buffer space and the possible number of spare parts in stock. The literature hardly even considers the evaluation of such systems. We are the first to introduce the joint optimization of buffer space and spare parts for flow lines of arbitrary length. First, we aim to allocate buffer capacities and spare parts efficiently. Since the buffer allocation problem is NP-hard, we can expect only to find near-optimal solutions. Second, we demonstrate the algorithmic behavior of different greedy and metaheuristics on this design problem. We illustrate how to exploit the problem structure to solve it almost optimally. Third, we generate managerial insights into allocating spare parts in manufacturing systems with buffers. We show that spare parts tend to be more effective when arranged at or near the center of a flow line, as it is already known for buffers. Moreover, we provide details on the combined buffer and spare part allocations.

Maximizing Energy Density of Full-Cell Storage Devices Using Topology Optimization

Typical porous electrodes are homogeneous, stochastic collections of micron-scale particles offering few opportunities for engineering higher performance. To leverage recent breakthroughs in advanced and additive manufacturing, we use topology optimization to design porous electrodes for electrochemical energy storage devices such as batteries and supercapacitors. Energy density is maximized, leading to non-trivial geometries that outperform monolithic electrodes. These geometries facilitate ionic transport and lead to better electrode utilization and energy efficiency. We consider simultaneous optimization of cathode and anode, which can lead to interdigitated/interpenetrating designs.This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was supported by the LLNL-LDRD program under project number 23-SI-002 and 20-ERD-019.LLNL-ABS-857450

(Invited) 3D Carbon-Based Hierarchical Electrodes for Energy Storage

: Carbon-based 3D hierarchical electrodes with appropriate spatial distributions of pores are expected to empower rapid and controllable electrochemical reactions. The ability to fabricate such an electrode, with tunable geometries comprised of continuous nano- and micro-conduits, allows simultaneous optimization of conversion capacity as well as reaction rate. Such manufacturability holds great potential to redefine the future of energy storage, electrocatalysis and chemical production for a sustainable future.In this presentation, I will outline two convergent strategies that involve the integration of additive manufacturing with several decades of advancements in nanomaterials synthesis, coupled with conventional pyrolysis – for creating such electrodes. The primary structure of 3D carbon-based electrodes was fabricated by either direct 3D writing, or the transition metal assisted pyrolysis of 3D-printed polymer templates. The secondary structure was established by employing bottom-up synthesis approaches: (a) SiO2 nanoparticle templates to create nanoscopic porous structures or (b) nanochannels formed through direct growth of carbon nanotubes. We have demonstrated that the primary and secondary structures can be formed by a single step. After monolithic integration of a tertiary structure to reveal sites for electrochemical conversion, these 3D architected electrodes offer impressive charge storage capacities while maintain excellent rate capability.By exploiting the short mass diffusion lengths, we have demonstrated that the new energy storage devices can operate at temperatures as low as -70°C without the requirement of a heater. Furthermore, the incorporation of a high loading of active materials in our 3D electrodes allows for concurrent enhancement of both energy density and power density. These scalable fabrication protocols address two critical challenges – sluggish mass transfer in 3D and the lack of accessible active sites, thereby offering a pathway to harness electrochemical reactions in 3D. This work offers an adaptable solution across a broad spectrum of industries.

Effect of foaming conditions and drying temperatures on total polyphenol content and drying rate of foam-mat dried banana powder: Modeling and optimization study

This study aims to optimize the parameters of the foam-mat drying to produce banana powder. Input parameters of the foam drying such as foaming agent (egg albumin) concentration used from 5 to 15 %, the foam stabilizer (maltodextrin) used from 1 to 3 % and the drying temperature varied between 60 and 80 °C. Factors with 3 levels are arranged according to the Box-Behnken design, which further modelled by RSM (response surface methodology) and ANN (artificial neural network), and optimized. The drying rate and total polyphenol content (TPC) of banana powder under the studied conditions were determined as the target output. The moisture, color, total polyphenol, antioxidant activity and some physical parameters of final fine powder were analyzed. Increasing temperature has increased the drying rate. In addition, increasing the concentrations of egg albumin and maltodextrin maintained the highest TPC and maximum drying rate. ANN model showed the higher forecasting capacity than that of RSM. Moreover, simultaneous optimization of two responses (TPC and drying rate) was selected to maximize the desired value at the concentration of albumin, maltodextrin and drying temperature of 11.86 %, 1.92 %, 74.94 °C, respectively, corresponding to the highest TPC value and drying rate of 1.31 mgGAE/g DW and 2.48 g water/g dry matter/min. At this condition, the drying time was recorded as 103 min. Validation of the optimal ratios showed that the experimental values of TPC and drying rate were in good agreement with the model predicted data. The moisture content and water activity of product were found to be 5.87 ± 0.07 % and 0.37 ± 0.01. The product had bright colors with L*, a* and b* values were measured as 86.4 ± 0.5, 1.75 ± 0.08 and 15.2 ± 0.3, respectively. The high DPPH radical scavenging activity was detected (53.5 %) with the water solubility index and water absorption index of banana powder was determined at value of 56.98 % and 5.17 g/g, respectively. Foam mat dried banana powder was well preserved in paper packaging with aluminum foil.

Synthesis of activity-based costing and deep learning to support cost management: A case study in the automotive industry

Activity based costing (ABC) models for cost management and big data analytics are deeply rooted in academia and practice to efficiently manage operational costs. Inspired by cost management and deep learning (DL) theories we develop a multilayer perceptron (MLP) using a nested particle swarm optimized (PSO) neural network algorithm for self-controlled architecture and weight optimization. This innovative approach enables to mimic ABC while increasing prediction accuracy. In a real-world case study we use wheel manufacturing data provided by a large original equipment manufacturer (OEM) and follow a full factorial experimental design. Furthermore, we benchmark our novel DL approach with results derived from a traditional ABC analysis. We demonstrate that this intelligent cost estimation model can mimic ABC using few cost drivers contributing to efficient and transparent interorganizational cost management. Major quantitative findings within a conducted field experiment demonstrate a high forecast accuracy with low absolute cost percentage error (CPE) deviation of the novel cost estimation approach in industry. The results extend the scope of PSO for the simultaneous optimization of the architecture and weights of neural networks. Additionally, we prove that the PSO algorithm is a suitable alternative to traditional optimization methods in DL.

Sensitivity analysis and robust optimization to high-dimensional uncertainties of compressors with active subspace method

The performance of compressors often deviates significantly from the design value due to the substantial geometric deviations arising from manufacturing errors and in-service degradation. Robust design optimization (RDO) plays a crucial role in ensuring the high performance and reliability of compressors. The study proposes an active subspace-based dimensionality-reduction RDO method for high-dimensional uncertainties of compressors. By employing the active subspace (AS) method, a dimensionality-reduction artificial neural network (ANN) is raised to solve the "dimension disaster" problem for high-dimensional uncertainty quantification (UQ) analysis. Additionally, to decrease the computation cost of the original AS method, a data-driven method is utilized to calculate the gradient of the quantity of interest (QoI) within the AS dimensionality-reduction framework. Furthermore, Shapley method is applied to study the sensitivity of geometric variables and their variation direction on the mean and variance of the performance, and the robustness improvement method by only modifying key geometric variables based on Shapley results is discussed. The RDO of high dimensional geometrical uncertainties of a transonic compressor stage is studied by using this method. The results demonstrate a reduction from the original 24-dimensional uncertainties to a 4-dimensional space, achieved through this method. Consequently, the required sample size is reduced by 75 % while maintaining nearly unchanged model accuracy. Furthermore, the findings reveal that after RDO, the mean efficiency increases by 1.04 %, with a 42 % reduction in variance. Compared to RDO, which requires simultaneous optimization of all 24 geometric variables, the robustness improvement based on Shapley only necessitates modifying 4 key geometric variables, resulting in a substantial increase in both the mean and variance of efficiency.

Structural evolution mechanism and magnetic properties of soft magnetic composites transformed from FeSiAl/Co3O4 composites

Simultaneous optimization of core loss and saturation magnetization in soft magnetic composites (SMCs) is crucial for the development of high frequency and high power in power electronic devices. In this work, FeSiAl/(Al2O3-Co) and FeSiAlCo/Al2O3 SMCs were prepared by sintering FeSiAl/Co3O4 composite powders at different temperatures. The corresponding structural evolution mechanism and magnetic properties of the prepared SMCs were studied systematically. When the sintering temperature was below 950 ℃, a chemical reaction at the FeSiAl/Co3O4 interface occurred and led to the formation of Al2O3-Co composite coating. The Al2O3 coating, as an isolation band, can effectively hinder the interdiffusion between Co coating and FeSiAl magnetic powder. When the sintering temperature increased to above 1000 ℃, the interdiffusion began to occur and was accelerated by the high temperature and chemical potential educed by concentration gradient, finally resulting in the formation of FeSiAlCo alloy. The prepares SMCs exhibited a low core loss due to the high integrity of Al2O3 coating. Its saturation magnetization and permeability significantly increased with the sintering temperature due to the generation of Co coating with ferromagnetism. This work provides a new strategy for the construction of ceramic oxide coating with high integrity and elemental doping of magnetic powder in SMCs, which is expected to be applied to the preparation of high-performance SMCs.

Maximization of Total Profit for Hybrid Hydro-Thermal-Wind-Solar Power Systems Considering Pumped Storage, Cascaded Systems, and Renewable Energy Uncertainty in a Real Zone, Vietnam

The study maximizes the total profit of a hybrid power system with cascaded hydropower plants, thermal power plants, pumped storage hydropower plants, and wind and solar power plants over one operation day, considering the uncertainty of wind speed and solar radiation. Wind speed and solar radiation in a specific zone in Vietnam are collected using the wind and solar global atlases, and the maximum data are then supposed to be 120% of the collection for uncertainty consideration. The metaheuristic algorithms, including the original Slime mould algorithm (SMA), Equilibrium optimizer, and improved Slime mould algorithm (ISMA), are implemented for the system. ISMA is a developed version of SMA that cancels old methods and proposes new methods of updating new solutions. In the first stage, the cascaded system with four hydropower plants is optimally operated by simulating two cases: simultaneous optimization and individual optimization. ISMA is better than EO and SMA for the two cases, and the results of ISMA from the simultaneous optimization reach greater energy than individual optimization by 154.8 MW, equivalent to 4.11% of the individual optimization. For the whole system, ISMA can reach a greater total profit than EO and SMA over one operating day by USD 6007.5 and USD 650.5, equivalent to 0.12% and 0.013%. The results indicate that the optimization operation of cascaded hydropower plants and hybrid power systems can reach a huge benefit in electricity sales

Regularization in space–time topology optimization for additive manufacturing

In additive manufacturing, the fabrication sequence has a large influence on the quality of manufactured components. While planning of the fabrication sequence is typically performed after the component has been designed, recent developments have demonstrated the possibility and benefits of simultaneous optimization of both the structural layout and the corresponding fabrication sequence. This is particularly relevant in multi-axis additive manufacturing, where rotational motion offers enhanced flexibility compared to planar fabrication. The simultaneous optimization approach, called space–time topology optimization, introduces a pseudo-time field to encode the manufacturing process order, alongside a pseudo-density field representing the structural layout. To comply with manufacturing principles, the pseudo-time field needs to be monotonic, i.e., free of local minima. However, explicitly formulated constraints proposed in prior work are not always effective, particularly for complex structural layouts that commonly result from topology optimization.In this paper, we introduce a novel method to regularize the pseudo-time field in space–time topology optimization. We conceptualize the monotonic additive manufacturing process as a virtual heat conduction process starting from the surface upon which a component is constructed layer by layer. The virtual temperature field, which shall not be confused with the actual temperature field during manufacturing, serves as an analogy for encoding the fabrication sequence. In this new formulation, we use local virtual heat conductivity coefficients as optimization variables to steer the temperature field and, consequently, the fabrication sequence. The virtual temperature field is inherently free of local minima due to the physics it resembles. We numerically validate the effectiveness of this regularization in space–time topology optimization under process-dependent loads, including gravity and thermomechanical loads.

Optimising design and operation of sewage source heat pump: techno-economic and environmental assessment

ABSTRACT Heat pump can be used to recover abundant thermal energy contained in the discharge of municipal wastewater treatment plants. While there are some design standards for common heat pump systems, the design of a sewage source heat pump (SSHP) system is still often based on a fixed heat load and neglects the interdependencies between the equipment sizing and operating parameters. To address the issue that previous design methods have not balanced investment and operational costs well from a global optimisation perspective, this work formulates the simultaneous optimisation of SSHP design and operation as a non-linear programming problem. The proposed model features the consideration of multiple working conditions caused by the impact of ambient temperature variation on the heat load of the SSHP system. The feasibility and potential benefits of the optimised SSHP system are also evaluated by incorporating techno-economic performances and environmental impact analyses into the mathematical framework. A case study is carried out to demonstrate the effectiveness of the proposed methodology. The results show that the total annual cost of the optimally designed and operated SSHP in Harbin could be 9% lower than in Beijing and 39% lower than in Shanghai, suggesting that constructing and running the SSHP system in severe cold regions with great heating demands might be more economical than in less cold regions. The CO2, SO2, and NOx emissions of the SSHP could be approximately 50% less than that of coal-fired boiler heating, and 80% less than that of direct electric heating with coal-fired electricity.

Plasma-modified Co3O4 nanosheets for efficient CO detection: Insight into the structure-activity relationship

Exploring the correlation between crucial reactive sites and surface electron structure in the sensitive layer of gas sensors can achieve simultaneous optimization of kinetics and thermodynamics. In this work, Ar and O2 plasma sputtering are used to study how varying levels of Co3+ and oxygen vacancy affect the gas-sensitivity of Co3O4. The gas-sensing performance of Co3O4-O2PT (O2 plasma sputtering) is better than that of Co3O4-ArPT (Ar plasma sputtering), which exhibits a high response value (100 ppm-4.16) and quick response-recovery time (1.5 s/38 s). The enhancement of gas-sensing performance benefits from a high Co3+/Co2+ ratio, which provides strong CO-selective adsorption and oxidation capabilities. Therefore, the interfacial oxidation reaction reaches equilibrium rapidly. This work provides a straightforward and controllable modification approach and enhances the understanding of how oxygen vacancies and Co3+ affect Co3O4 gas sensors, which promotes the design and development of high-performance cobalt-based gas sensors.

Eliminating enclosed voids in simultaneous optimization of topology and bi-material distribution of three-dimensional structures for additive manufacturing

Eliminating enclosed voids in simultaneous optimization of topology and bi-material distribution of three-dimensional structures for additive manufacturing

Simultaneous stochastic optimisation of mining complexes with equipment uncertainty: Application at an open-pit copper mining complex

Simultaneous stochastic optimisation of mining complexes with equipment uncertainty: Application at an open-pit copper mining complex

Feasibility of simultaneous optimization of Anammox start-up and nitrogen removal performance by intermittent dosing of nanoscale zero-valent iron

The long acclimation period and sensitivity to environmental conditions of Anammox are the bottlenecks for its promotion and application. An innovative strategy was adopted to accelerate functional microbial enhancement and improve nitrogen removal performance by inoculating cryopreserved Anammox sludge and activated sludge with intermittent dosing of nanoscale zero-valent iron (nZVI). The acclimation time was shortened by 76 days with nitrogen removal efficiency (NRE) reaching up to 91.07 %. Anammox, NDFO (nitrate/nitrite-dependent Fe(II) oxidation), Feammox (Fe(III) reduction coupled with anaerobic ammonium oxidation) and abiotic reactions were coupled in the system with nZVI, contributing to 69.79 %, 15.14 %, 9.84 % and 0.25 % of nitrogen removal, respectively. Further microbial analysis demonstrated significant enrichment of functional microorganisms, such as Candidatus Jettenia, Acidovorax and Comamonas. High-efficient nitrogen removal was attribute to the increase of functional genes involved in Anammox, electronic transfer, heme C synthesis and iron metabolism. This work provides an inspiring idea for the mainstream Anammox application.

Predictive functional control for chamber pressure: A quantum simultaneous whale optimization approach

This study introduces a Constrained Predictive Functional Controller (CPFC) designed for regulating chamber pressure in a coke furnace. While the traditional controllers face challenges posed by the system’s complex multi-input multi-output (MIMO) structure, cross-coupling, time delays, physical constraints, and uncertainties, this article’s approach realized set-point tracking and disturbance rejection, addressing model mismatches while considering constraints on system inputs, input variations, and outputs. To tackle the constrained Model Predictive Control (MPC) optimization problem and minimize the cost function, the Quantum Simultaneous Whale Optimization Algorithm (QSWOA) is employed. This meta-heuristic optimization method integrates quantum coding and simultaneous search with the whale optimization algorithm, enhancing convergence speed and precision. Furthermore, the performance of the proposed CPFC-QSWOA controller is assessed through simulations of the chamber pressure control of a Coke furnace. Results demonstrate the superiority of the CPFC-QSWOA approach, showcasing high precision and robustness, particularly in the face of uncertainties and disturbances.