Sequential Optimization Research Articles

Joint Decisions of Components Replacement and Spare Parts Ordering Considering Different Supplied Product Quality

In addition to equipment maintenance decisions, spare parts ordering decisions from different suppliers play a key role in reducing related costs (e.g., maintenance, inventory and ordering costs). Since suppliers may use different production technologies and materials, spare parts (or products) from different suppliers can be different in quality. Nevertheless, in recent studies, the quality of spare parts is rarely considered to incorporate both equipment maintenance and spare parts ordering. In this paper, we investigate the joint optimization of condition-based maintenance and spare parts provisioning policy under two suppliers with different product quality. We formulate a sequential-decision problem with a Markov decision process and consequently obtain an optimal maintenance and ordering policy by an exact value iteration algorithm. To improve computation efficiency, based on the principle of sequential optimization, we develop heuristic methods. Extensive numerical experiments are conducted to assess the overall performance of the developed heuristic methods. Compared to the optimal method, results showed that the average cost gap is about 2% and computation time is reduced by 94% on average under the proposed heuristic method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the observation that automobile industries tried to integrate emergency suppliers from which spare parts have different quality into maintenance schedules to avoid stockout and reduce equipment failure during the Covid-19 pandemic. Specifically, the article focuses on balancing the trade-offs between condition-based maintenance and inventory management from two suppliers with different lead times and spare parts quality for multi-unit systems. On the one hand, effective maintenance scheduling relies on spare parts for replacement to ensure the stability of production. On the other hand, inventory management needs to select the supplier with appropriate lead time and product quality to reduce the ordering cost and avoid stockout based on the degradation states of equipment. The joint optimization of these two aspects serves to reduce the total maintenance and ordering cost. Nevertheless, most existing research aims to optimize them separately. In this paper, we formulate the joint decision problem considering the two aspects based on a Markov decision process. We obtain an optimal maintenance and ordering policy by an exact value iteration algorithm and present heuristics to improve the computation efficiency when the system contains multiple machines. Practitioners can implement the proposed methodology to make condition-based maintenance and inventory management when spare parts with different qualities are ordered from two suppliers. To balance cost and computational efficiency, it is suggested to implement the optimal policy by an exact value iteration algorithm when the number of machines is small in the system and use the heuristic methods when the number of machines is large (i.e., usually larger than 3).

Transferability of Multi-Objective Neuro-Fuzzy Motion Controllers: Towards Cautious and Courageous Motion Behaviors in Rugged Terrains

This study is motivated by the need to develop generic neuro-fuzzy motion controllers for autonomous vehicles that may traverse rugged terrains. Three types of target problems are investigated. These problems differ in terms of the expected motion behavior, including cautious, intermediate, and courageous behaviors. The target problems are defined as evolutionary multi-objective problems aiming to evolve near optimal neuro-fuzzy controllers that can operate in a variety of scenarios. To enhance the evolution, sequential transfer optimization is considered, where each of the source problems is defined and solved as a bi-objective problem. The performed experimental study demonstrates the ability of the proposed search approach to find neuro-fuzzy controllers that produce the required motion behaviors when operating in various environments with different motion difficulties. Moreover, the results of this study substantiate the hypothesis that solutions with performances near the edges of the obtained approximated bi-objective Pareto fronts of the source problems provide better transferability as compared with those that are associated with performances near the center of the obtained fronts.

RL-SeqISP: Reinforcement Learning-Based Sequential Optimization for Image Signal Processing

Hardware image signal processing (ISP), aiming at converting RAW inputs to RGB images, consists of a series of processing blocks, each with multiple parameters. Traditionally, ISP parameters are manually tuned in isolation by imaging experts according to application-specific quality and performance metrics, which is time-consuming and biased towards human perception due to complex interaction with the output image. Since the relationship between any single parameter’s variation and the output performance metric is a complex, non-linear function, optimizing such a large number of ISP parameters is challenging. To address this challenge, we propose a novel Sequential ISP parameter optimization model, called the RL-SeqISP model, which utilizes deep reinforcement learning to jointly optimize all ISP parameters for a variety of imaging applications. Concretely, inspired by the sequential tuning process of human experts, the proposed model can progressively enhance image quality by seamlessly integrating information from both the image feature space and the parameter space. Furthermore, a dynamic parameter optimization module is introduced to avoid ISP parameters getting stuck into local optima, which is able to more effectively guarantee the optimal parameters resulting from the sequential learning strategy. These merits of the RL-SeqISP model as well as its high efficiency are substantiated by comprehensive experiments on a wide range of downstream tasks, including two visual analysis tasks (instance segmentation and object detection), and image quality assessment (IQA), as compared with representative methods both quantitatively and qualitatively. In particular, even using only 10% of the training data, our model outperforms other SOTA methods by an average of 7% mAP on two visual analysis tasks.

An efficient tool to find multispecies MSY for interacting fish stocks

AbstractNatural ecological communities exhibit complex mixtures of interspecific biological interactions, which makes finding optimal yet sustainable exploitation rates challenging. Most fisheries management advice is at present based on applying the Maximum Sustainable Yield (MSY) target to each species in a community by modelling it as if it was a monoculture. Such application of single‐species MSY policies to strongly interacting populations can result in tragic overexploitation. However, the idea of ‘maximising the yield from each species separately’ can be extended to take into account species interactions. This leads to a form of Nash Equilibrium, where the yields of each species are simultaneously maximised. Here we present ‘nash’, an R package that streamlines the computation of Nash equilibrium reference points for fisheries and other systems represented by a user‐defined multispecies or ecosystem model. We present two real‐world fisheries management applications alongside performance benchmarks. Satisfactory search results are shown across models with an approximate factor 7 increase in performance when compared to the expensive round‐robbing sequential optimisation algorithms used by other authors in the literature. We believe that the nash package can play an instrumental role in fully implementing ecosystem‐based management policies worldwide.

Identification and characterization of chitinase producing marine microorganism: Unleashing the potential of chitooligosaccharides for bioethanol synthesis

The dwindling supply of the petroleum product and its carbon footprint has initiated search for a sustainable fuel and alternate feed-stocks. One such underexplored feedstock is chitin, a waste derived from sea food processing. The limitation of insolubility and crystallinity inherent in chitin is addressed with the chitin hydrolysates. In the present study, a chitinases producing marine isolate was isolated from the sediments of Arabian Sea from a depth of 20 m. In order to increase the expression of the chitinases, sequential optimisation using one factor at a time and Taguchi experimental designs were employed which resulted in a yield of 13.46 U/mL which was 2.62 fold higher than the initial bioprocess condition values. In a two-step refinery protocol, Candida albicans was evolved towards chitooligosaccharides using chemically synthesized hydrolysates. In a fed –batch fermentation design the Candida yielded a 12.8 % conversion of these commercial chitin oligosaccharides into bioethanol in a run time of 48 h. This is the first report demonstrating the potential of Candida to utilise chitin oligosaccharides for the production of bioethanol.

Integrated Bus Timetabling and Scheduling with a Mutation-Based Evolutionary Scheme Maximizing Headway Quality and Connections

Public transport planning is a multi-level process that includes various complex tasks. These tasks are traditionally executed sequentially, and the result of each task serves as input for consecutive tasks. A simultaneous integrated consideration of multiple tasks may lead to an overall improved solution, but further increase the complexity of already hard-to-solve planning problems. This work focuses on timetabling and vehicle scheduling and evaluates synergies from the integrated optimization. We investigate an exact sequential, exact integrated, and heuristic approach to solve the combined problem for large public transport networks considering the interlining of vehicles, multiple vehicle types, or multiple depots while additionally aiming to maximize regular “clock-faced’’ headways and transfer connections. Compared to sequential optimization, an integrated approach significantly reduces nominal and operational costs while maintaining high service quality. However, an exact integrated approach is only able to compute solutions for problems of limited size in a reasonable time. We propose an adaptive modular evolutionary extendable scheme that effectively balances computational efficiency and solution quality. By utilizing various problem-specific mutation operators and adaptively applying them based on their impact, the heuristic can compute high-quality solutions for large real-world-inspired public transport networks in a reasonable time while considering short connecting times between lines and regular clock-faced headways.

Production, characterization and biomedical potential of biosurfactants produced by haloalkaliphilic archaea from Wadi El-Natrun, Egypt

Extreme halophilic archaea that can live in high saline environments can offer potential applications in different biotechnological fields. This study delves into the fascinating field of halophilic archaea and their ability to produce biosurfactants. Some strains of haloarchaea were isolated from Wadi El-Natrun and were screened for biosurfactants production in a standard basal medium using emulsification index assay. Two strains were chosen as the potential strains for surface tension reduction. They were identified as Natrialba sp. BG1 and N3. The biosurfactants production was optimized and the produced emulsifiers were partially purified and identified using FTIR and NMR. Sequential statistical optimization, Plackett–Burman (PB) and Box–Behnken Designs (BBD) were carried out using 5 factors: oil, NaCl, casamino acids, pH, and inoculum size. The most significant factors were used for the next Response Surface Methodology experiment. The final optimal conditions for biosurfactants production were the inoculum size 2% pH 11 and NaCl 250 g/L, for Natrialba sp. BG1 and inoculum size 2.2%, pH 10 and NaCl 100 g/L for Natrialba sp. N3. The produced biosurfactants were tested for wound healing and the results indicated that Natrialba sp. BG1 biosurfactants is more efficient than Natrialba sp. N3 biosurfactants. Biosurfactants extracts were tested for their cytotoxic effects on normal cell line as well as on different cancer cells using MTT assay. The findings demonstrated that varying concentrations of the biosurfactants (31.25, 62.5, 125, 250, 500 and 1000 µg/mL) exhibited cytotoxic effects on the cell lines being tested. Additionally, the outcomes unveiled the presence of anti-inflammatory and antioxidant properties for both biosurfactants. Consequently, they could potentially serve as natural, safe, and efficient novel agents for combating cancer, promoting wound healing, and providing anti-inflammatory and antioxidant benefits.

Sequentially optimized process towards sustainable synthesis of activated carbon from wild thornbush for 4-nitrophenol and industrial effluent treatment.

Environmental nuisance thornbush Prosopis juliflora was utilized as an inexpensive and renewable biomass raw material for the sustainable production of activated carbon. Previously, the sequential muffle furnace-microwave arrangement was effective with acid activation for activated carbon synthesis. However, the intermediate synthesis steps were not optimized. In this work, we have optimized the intermediate steps, viz. chemical impregnation, carbonization, and microwave activation. Sequential optimization for base activation was developed and compared with acid activation. The base-activated carbon (BAC) exhibited a more crystalline nature and faster uptake kinetics than AAC. BAC demonstrated an adsorption capacity of 576mg/g for 4-nitrophenol (4-NP) surpassing that of optimized acid-activated carbon (AAC) by 45%. The optimal base activation required 1.85 times lower microwave energy than that of the acid activation. BAC exhibited significantly higher BET surface area (1319 m2/g) and micropore volume (0.524 cm3/g) which were about 28% and 26% higher than those of AAC. When compared to biochar obtained from the same thornbush, the BAC exhibited an 11-fold increase in adsorption capacity. The adsorbents could be easily regenerated with ethanol and used up to five cycles. Adsorption using BAC also could achieve 80% COD removal for industrial wastewater, while AAC led to 61% removal. Continuous packed column with BAC revealed a breakthrough time of 3.5h for industrial effluent while for 500mg/L 4-nitrophenol, it was 25h. Prosopis juliflora thornbush, an environmental nuisance, could be converted into a high-capacity adsorbent for environmental remediation after careful sequencing and optimization of the intermediate synthesis steps.

FLC-Based Ultra-Low-Frequency Oscillation Suppression Scheme for Interconnected Power Grids

The appearance of ultra-low-frequency oscillations in the grid at the sending end, after asynchronous grid interconnection, poses a significant threat to the stable operation of the system. For post-asynchronous interconnection in a multi-DC transmission system, an investigation is conducted to analyze the causes of ultra-low-frequency oscillations and the utilization of a Frequency Limit Controller (FLC) which aims to suppress these oscillations. Furthermore, a method is developed to rank DC sensitivity, considering the hydroelectric distribution in the sending-end grid, by combining the DC FLC impact factor and DC control sensitivity. Subsequently, a novel approach for ultra-low-frequency oscillation suppression is proposed. This approach employs the stochastic subspace method for parameter estimation and the NSGA-II optimization algorithm to convert the multi-DC optimization challenge into multiple sequential cyclic optimization problems, each focusing on a single DC, ensuring a more effective suppression of ultra-low-frequency oscillations. The proposed scheme’s effectiveness is validated through simulations using a specific locations’ interconnected power grid.

Co-Optimization of Speed Planning and Power Management for Automated HEVs in Unstructured Road Scenarios

With the development of intelligent and connected vehicle technology, the simultaneous optimization of powertrain performance and vehicle motions provides an unprecedented perspective to further explore the energy-saving potential of hybrid electric vehicles (HEV). However, it is still very challenging because of the complex driving environment and large computational burdens, and hence, the co-optimization strategy under unstructured scenarios has rarely been studied. For unstructured road driving scenarios, this paper proposes a hierarchical optimal control system combing speed planning and energy management for an autonomous HEV. In the first layer, a vehicle stability constraints system is designed with consideration of both external road characteristics and vehicle dynamics to limit the longitudinal speed, preventing the vehicle from excessive yaw, sideslip, and rollover during a complex, unstructured road trip. Then, the improved gray wolf optimizer is designed to efficiently generate optimal speed and SOC trajectories to guide the vehicle motion behavior and power split while ensuring driver safety. In the second layer, a mechanical implementation system is applied to fulfill the control system framework. Finally, the performance of proposed co-optimization is comprehensively verified in terms of fuel economy, vehicle maneuverability, computational effectiveness, and adaptability. Compared with the sequential optimization, the improvements by co-optimization range from 9.38% to 13.03% in fuel economy and from 0.31% to 3.15% in vehicle maneuverability under different test cycles, while ensuring real-time capability.

Stable Linear System Identification With Prior Knowledge by Riemannian Sequential Quadratic Optimization

Stable Linear System Identification With Prior Knowledge by Riemannian Sequential Quadratic Optimization

Treatment Algorithm for the Resorption of Calcific Tendinitis Using Extracorporeal Shockwave Therapy: A Data Mining Study.

Although evidence indicates that extracorporeal shockwave therapy (ESWT) is effective in treating calcifying shoulder tendinitis, incomplete resorption and dissatisfactory results are still reported in many cases. Data mining techniques have been applied in health care in the past decade to predict outcomes of disease and treatment. To identify the ideal data mining technique for the prediction of ESWT-induced shoulder calcification resorption and the most accurate algorithm for use in the clinical setting. Case-control study. Patients with painful calcified shoulder tendinitis treated by ESWT were enrolled. Seven clinical factors related to shoulder calcification were adopted as the input attributes: sex, age, side affected, symptom duration, pretreatment Constant-Murley score, and calcification size and type. The 5 data mining techniques assessed were multilayer perceptron (neural network), naïve Bayes, sequential minimal optimization, logistic regression, and the J48 decision tree classifier. A total of 248 patients with calcified shoulder tendinitis were enrolled in this study. Shorter symptom duration yielded the highest gain ratio (0.374), followed by smaller calcification size (0.336) and calcification type (0.253). With the J48 decision tree method, the accuracy of 3 input attributes was 89.5% by 10-fold cross-validation, indicating satisfactory accuracy. A treatment algorithm using the J48 decision tree indicated that a symptom duration of ≤10 months was the most positive indicator of calcification resorption, followed by a calcification size of ≤10.82 mm. The J48 decision tree method demonstrated the highest precision and accuracy in the prediction of shoulder calcification resorption by ESWT. A symptom duration of ≤10 months or calcification size of ≤10.82 mm represented the clinical scenarios most likely to show resorption after ESWT.

Enhancing clay content estimation through hybrid CatBoost-GP with model class selection

Excessive soil swelling, triggered by variations in moisture content, is recognized as one of the most severe geo-hazards. Swelling Potential (SP) is closely correlated with Clay Content (CC), Plasticity Index (PI), and Activity (A = PI/CC), which are crucial input features for SP prediction models. Traditional methods for determining CC, such as the hydrometer and pipette methods, are labor-intensive and time-consuming. Employing easily accessible soil properties to predict CC directly not only enhances the modeling efficiency of SP prediction models but also mitigates the impact of laboratory test errors on model performance. This study leveraged two databases encompassing a broad spectrum of soil classifications and developed three ensemble learning models (RF, XGBoost, and CatBoost) to predict CC efficiently. Sequential model-based optimization algorithms (SMBO) were employed to effectively identify the optimal hyperparameters for these models. To further refine the model, the Shapley additive explanation (SHAP) method was utilized to ascertain the most influential soil properties. The findings revealed that the CatBoost model, optimized via GP, achieved the highest level of predictive accuracy. Three easily measurable soil properties — the percentage of soil particles passing through 0.075 mm and 0.425 mm sieves, along with the plasticity index — were identified as key predictors for CC. The estimated CC values were instrumental in constructing various empirical and machine learning models to predict SP, all of which exhibited exceptional performance. This research advances the modeling efficiency of SP prediction models, making it particularly relevant for rapid-response scenarios in transportation infrastructure projects.

A Collaboratively Iterative Sequential Approximate Optimization Method for Aerodynamic Optimization

A Collaboratively Iterative Sequential Approximate Optimization Method for Aerodynamic Optimization

Performance analysis of data resampling on class imbalance and classification techniques on multi-omics data for cancer classification.

Cancer, in any of its forms, remains a significant public health concern worldwide. Advances in early detection and treatment could lead to a decline in the overall death rate from cancer in recent decades. Therefore, tumor prediction and classification play an important role in fighting cancer. This study built computational models for a joint analysis of RNA seq, copy number variation (CNV), and DNA methylation to classify normal and tumor samples across liver cancer, breast cancer, and colon adenocarcinoma from The Cancer Genome Atlas (TCGA) dataset. Total of 18 machine learning methods were evaluated based on the AUC, precision, recall, and F-measure. Besides, five techniques were compared to ameliorate problems of class imbalance in the cancer datasets. Synthetic Minority Oversampling Technique (SMOTE) demonstrated the best performance. The results indicate that the model applying Stochastic Gradient Descent (SGD) for learning binary class SVM with hinge loss has the highest classification results on liver cancer and breast cancer datasets, with accuracy over 99% and AUC greater than or equal to 0.999. For colon adenocarcinoma dataset, both SGD and Sequential Minimal Optimization (SMO) that implements John Platt's sequential minimal optimization algorithm for training a support vector machine shows an outstanding classification performance with accuracy of 100%, AUC, precision, recall, and F-measure all at 1.000.

Two-level optimization strategy for vehicle speed and battery thermal management in connected and automated EVs

The performance of the battery is affected by temperature, and the battery thermal management (BTM) system consumes considerable energy to maintain the temperature in the suitable range. The unnecessary acceleration and deceleration of electric vehicles (EVs) during driving causes higher energy consumption in the powertrain. The emergence of connected and automated vehicle (CAV) technology provides an opportunity for predictive control of thermal and energy management. To explore the coordination optimization between battery thermal and vehicle energy management, this article proposes a two-level optimization framework for the speed and BTM of EVs, which improves energy efficiency and battery safety. Each level consists of a sequential optimization of speed and battery thermal. In the upper layer, speed planning based on iterative dynamic programming (IDP) is first proposed to reduce powertrain energy consumption using intelligent traffic information. Then, based on the BTM system and the battery thermodynamics features, the long-term optimal trajectory of the battery temperature is derived according to optimized speed. In the lower layer, the model predictive controllers (MPC) are designed to track reference speed and temperature trajectories in real-time and enforce energy saving. Meanwhile, to improve the prediction accuracy of the system model, we integrate the Gaussian process (GP) model in the MPC and build the learning-based MPC strategy. Simulation results verify the performance of the proposed method which reduces the powertrain energy consumption by 20.95%. In the high and low temperature environment, compared with normal MPC, PID-based and Rule-based, it reduces BTM energy consumption by up to 15.69%, 29.68% and 38.73%.

RBF-Based Integrated Optimization Method of Structural and Turning Parameters for Low-Floor Axle Bridge

The axle bridge plays a crucial role in the bogie of low-floor light rail vehicles, impacting operational efficiency and fuel economy. To minimize the total cost of the structure and turning of axle bridges, an optimization model of structural and turning parameters was built, with the fatigue life, maximum stress, maximum deformation, and maximum main cutting force as constraints. Through orthogonal experiments and multivariate variance analysis, the key design variables which have a significant impact on optimization objectives and constraints (performance responses) were identified. Then the optimal Latin hypercube design and finite element simulation was used to build a Radial Basis Function (RBF) model to approximate the implicit relationship between design variables and performance responses. Finally, a multi-island genetic algorithm was applied to solve the integrated optimization model, resulting in an 8.457% and 1.1% reduction in total cost compared with the original parameters and parameters of sequential optimization, proving the effectiveness of the proposed method.

Two-stage recoverable robust optimization for an integrated location–allocation and evacuation planning problem

We consider an integrated location–allocation and evacuation planning problem in a disaster context, where the effects of a disaster, including the uncertain capacities of relief facilities (rescue centers and distribution centers), uncertain demands for relief supplies and casualty treatment services, and uncertain availability of transportation links are characterized by a discrete scenario set. Instead of complete failures, we allow the disrupted relief facilities only lose part of their capacity. To deal with the uncertainties, we propose a two-stage recoverable robust optimization model, where the location decision of relief facilities, the allocation decision of delivering relief supplies from relief facilities to affected areas, the transfer decision of transporting casualties from affected areas to rescue centers etc are defined in two stages where the first-stage solution should be robust against the possible effects of a disaster that are revealed in the second stage, and the second-stage solution involves some recovery actions, which we term as multi-mitigation strategies: re-opening and re-operation, re-allocation, and relief supply sharing, to mitigate the effects. To solve the model to optimality, we develop a nested two-stage decomposition algorithm that iterates between a master problem considering only a subset of disaster scenarios solved by a Benders decomposition algorithm that incorporates some non-trivial acceleration strategies, and an adversarial separation problem that identifies disaster scenarios to enhance the worst-case recovery cost of the master problem. We introduce some warm-start techniques to accelerate the convergence of the solution algorithm. We conduct numerical studies on simulation instances to assess the performance of the solution algorithm, and analyze the robustness and recoverability of the model. We also conduct extensive numerical studies on realistic instances from Ya’an and Ganzi to demonstrate the benefits of accounting for recoverable robustness over a stochastic policy and a robust policy without recovery actions, the benefits of considering integrated optimization over sequential optimization, and the benefits of considering partial capacity loss and multi-mitigation strategies.

Sequential simulated annealing for life-cycle optimization of nonlinear stochastic systems via arbitrary polynomial chaos expansion

Quantifying the uncertainties of engineering systems modeled as nonlinear oscillators subject to random excitation is a theoretically complex and computationally demanding task. Consequently, finding an optimal design of such systems considering their life-cycle performance is prohibitive with Monte Carlo simulation methods. In this paper, an efficient performance-based design optimization approach is developed for finding the optimal parameters of engineering systems modeled as nonlinear/hysteretic oscillators subject to stationary and non-stationary excitation. This novel approach utilizes an arbitrary polynomial chaos expansion to estimate the expected cost of failure without performing a computationally expensive integration over the hazard levels. Moreover, we introduce a novel sequential heuristic optimization scheme based on simulated annealing to minimize the total expected cost over the structure life-cycle. Three examples are included in the paper to assess the developed optimization scheme. First, we use the developed framework to optimize a linear single-degree-of-freedom oscillator subject to broadband excitation. Second, a multi-degree-of-freedom oscillator with cubic nonlinearity in damping and stiffness, subject to stationary broadband excitation, is optimized to show the influence of the problem dimensionality in the optimization process. In the last example, a multi-story reinforced concrete shear building modeled as a multi-degree-of-freedom Bouc-Wen oscillator with stiffness and strength degradation and subject to multi-hazards modeled as stationary (wind excitation) and non-stationary (earthquake) stochastic processes, is optimized.

Evaluation and control of energy‐efficient processes for separating epichlorohydrin‐oriented quaternary system

AbstractBACKGROUNDThe direct epoxidation of allyl chloride is a clean and efficient synthesis method for producing epichlorohydrin (ECH), which is a crucial organic intermediate widely used in the fields of renewable energy and aerospace. However, the presence of multiple azeotropes in the synthesized product using this method complicates the separation process and results in high energy consumption. To efficiently separate high‐purity ECH, this paper investigates and analyzes three separation schemes based on phase‐equilibrium analysis: hybrid extractive distillation (HED), pressure swing distillation (PSD) and three‐column batch distillation (TCBD).RESULTSThe operating parameters of the three separation processes are optimized by the sequential iterative optimization method such that the minimum total annual cost can reach $493 491 yr−1. The thermal integration method is used for process energy‐saving optimization, and the total annual cost can be further reduced by 6.9%. In addition, a comprehensive evaluation based on economic, energy, environmental and exergy analysis is conducted, and reveals that the TCBD process with thermal integration is optimal. A control structure is designed for the TCBD process to enhance its robustness such that the purity of ECH remains above 99.9 mol% under ±10% disturbances in feed flowrate and composition.CONCLUSIONSCompared with the HED and PSD processes, the TCBD process has better economic and environmental benefits, and its control structure can effectively resist disturbances. It is reasonable to believe that the TCBD process can be an excellent solution for the industrial production of ECH. © 2024 Society of Chemical Industry (SCI).