Heat Exchanger Network Synthesis Research Articles

A novel stochastic algorithm-based non-structural model for combined heat and mass exchanger network synthesis

A novel stochastic algorithm-based non-structural model for combined heat and mass exchanger network synthesis

Synthesis of flexible inter-plant heat exchanger networks: A decomposition method considering intermedium fluid circles

The traditional methods for synthesizing flexible heat exchanger networks (HENs) are not directly applicable to inter-plant HEN challenges, primarily due to the spread of system uncertainty across plants via intermedium fluid circles. This complicates the synthesis process significantly. To tackle this issue, this study proposes a decomposed stepwise methodology to facilitate the flexible synthesis of the inter-plant HENs performing indirect heat integration. A decomposition strategy is proposed to divide the overall network into manageable sub-networks by dissecting the intermedium fluid circles. To address the variability in intermedium fluid temperatures, a temperature fluctuation analysis approach is developed and a heuristic rule is introduced to maintain the temperature feasibility of the intermedium fluids. To ensure adequate flexibility and cost-effectiveness of the designed networks, flexibility analysis and network retrofit steps are conducted through model-based optimization techniques. The efficacy of the method is demonstrated through two case studies, showing its potential in achieving the desired operational flexibility for inter-plant HENs.

A novel optimization framework integrating multiple initialization, automatic topologization and MINLP reduction to accelerate large-scale heat exchanger network synthesis

Ensuring the solution feasibility and achieving an acceptable solution time for the non-deterministic polynomial-time hard (NP-hard) problem of heat exchanger network (HEN) synthesis is still challenging. In this study, we developed a novel optimization framework for efficiently solving large-scale HEN synthesis problems to obtain near global optimal solutions, simultaneously addressing the solution's infeasibility and time. The optimization framework consists of a multiple initial HEN design model (M-INI model), a new distance-based auto structure transformer (A-STR transformer), and a reduced non-linear programming model (R-NLP model). The M-INI model generates various HEN designs based on pinch analysis by varying the minimum approach temperature and the modes of stream splitting and mixing. The A-STR transformer is first proposed to automatically convert various HEN designs into associated superstructure-based topologies as multiple initial starting points for accelerating the solution of the R-NLP model. A strategy for approximating the temperature difference calculation is further applied to reduce the computational loads. Four case studies were conducted using the proposed method to minimize the total annual cost (TAC). The solutions of general cases 1 and 2 are almost equal to the best results in the literature, while the solution time is reduced by 98.7 % and 99.8 %, respectively, which demonstrates the performance of the proposed method. Especially, the method was applied to two practical industrial HEN cases: a large-scale low-grade heat recovery system and a crude distillation unit. The results of large-scale case 3 show that the optimal solution near the global optimal solution only requires 251.7 s. Case 4 includes pre-splitting in scenario A and free splitting in scenario B. Compared to scenario A, the TAC and solution time in scenario B were reduced by 33.9 % and 93.1 %, indicating that the pre-splitting strategy excludes the optimal from the search space. The proposed novel optimization framework demonstrates the efficiency and applicability of handling large-scale HEN synthesis problems. It can also guide engineers in the design of a large-scale HEN with lower costs and effort.

Simultaneous process optimization and heat integration for ethylene-to-ethylene oxide process: A surrogate model-based approach

Since the production process of ethylene to ethylene oxide (EO) is highly energy-intensive, it is necessary to perform a whole process heat integration. However, due to the technical barriers, it is always a challenge to design the heat integration system while optimizing the process parameters by mechanistic models. In response to this issue, this study presented a novel surrogate model-based framework that allows for the seamless integration of process optimization and heat exchanger network (HEN) design. The EO production process is modeled and simulated to provide the essential data for surrogate model training. Considering the inherent complexity of the process and the challenges associated with model training, the whole production process is decomposed into parts and the output parameters relevant to heat integration are included within the surrogate models training by Artificial Neural Network. Building upon these preliminary steps, a flexible optimization framework built upon the Genetic Algorithm, integrating the developed process surrogate model and any HEN synthesis model, is constructed to achieve synchronous optimization. The results show that the obtained process saves 22.93 % utility cost and finally leads to a 2.60 % increase in total profit than the alternative designed by the conventional stepwise method, showing the priority of the proposed method.

A penalty-free hybrid algorithm framework based on feasible stream matching principle for large-scale heat exchanger networks synthesis

In this work, a penalty-free hybrid stochastic-deterministic algorithm framework is proposed for large-scale heat exchanger networks (HENs) synthesis (HENS), formulated as a computationally-hard mixed-integer nonlinear programming (MINLP) problem. In the outer level, an improved genetic algorithm (GA) is developed to optimize process stream matches represented by integer variables whose values are generated by a unique heat exchanger vector. Unlike previous researches, the improved GA does not rely on any penalty terms, because we propose a feasible stream matching principle to exclude all infeasible process stream matches and only feasible matches are considered in optimization process. In the inner level, a reduced-size MINLP model is solved using deterministic methods to minimize total annualized costs (TACs), which are then used to evaluate the fitness of candidate HENs. Through this way, the proposed framework combines deterministic and stochastic methods to enhance optimization efficiency and global search capability. Illustrative tests on six benchmark cases demonstrate that the framework can efficiently achieve lower-cost solutions compared to deterministic, stochastic, or hybrid methods. The results show a decrease in TAC for all six cases and a reduction in solution time ranging from 11.1% to 97.2%. Importantly, the proposed framework can be extended to solve MINLP problems in other process networks.

Globally optimal simultaneous heat exchanger network synthesis and basic heat exchanger design

Globally optimal simultaneous heat exchanger network synthesis and basic heat exchanger design

Super-targeting of non-minimal WHEN systems

Energy conservation and emission reduction have become critical issues due to increasing environmental policy pressures and high energy prices. Work and heat exchange network (WHEN) synthesis is a promising approach to enhance the energy efficiency of energy-intensive processes. WHEN considering single-stage compression/expansion can be divided into two classes by structure complexity: minimal structure and non-minimal structure. The synthesis problem of minimal structure has been addressed in our previous work, where the global optimal solution can be guaranteed. In contrast, the compressors/expanders are not at the beginning or end of the streams' paths in the non-minimal structure and therefore the inlet/outlet temperatures are now variables of the problem. Instead of solving this complicated problem directly, we propose a super-targeting model to screen and scope the solution space for primal promising options before detailed structure design which can be designed using regular HEN synthesis methods. The super-target is estimated by regulating the thermodynamic paths of hot and cold streams where the objective function uses classical HEN super-target to evaluate the costs of the heat exchanger network. Benefiting from the unimodal nature of non-minimal WHEN super-targeting problems, we develop two deterministic algorithms: one relies on derivatives and one is derivative-free. Case studies show the efficiency of the proposed algorithm. The resulting TAC can be 23.1% lower than the literature report and the solution of super-targeting problems are proven to be globally optimal. The derivative optimization algorithm has been found to exhibit superior efficiency in solving problems of relatively large scale.

An innovative damping random walk algorithm with compulsive evolution for the synthesis of heat exchanger networks

Heat exchanger network synthesis in process system engineering poses a significant challenge, primarily because of the intricacies of stream matches and the nonlinear nature of continuous variables. Numerous heuristic methods are frequently trapped in local optima because of their greedy acceptance criteria, which limits their capacity to explore diverse solutions. This aspect of enhancing algorithms through acceptance Criterion has yet to be considered in previous studies. Hence, a damping strategy using damping perfect solutions is proposed and applied to the Random Walk Algorithm with Compulsive Evolution, which has recently received considerable attention. This strategy aims to prevent trapping in local optima and improve global search capabilities, thereby reducing the risk of premature convergence to the local optima. To address the impact of different damping coefficients within the strategy on the optimization process and structural variations in the algorithm, three damping strategies were designed: a fixed damping strategy with a constant damping coefficient, a variable damping strategy featuring variable damping coefficients at different optimization stages, and an adaptive damping strategy that tracks structural changes and selectively delays modifications driven by continuous variables to preserve those driven by integer variables. The results obtained from applying these strategies to three classical industrial HEN cases are 2,890,884 $/yr, 6,650,082 $/yr, and 1,709,205 $/yr, respectively. These results outperformed the optimal outcomes documented in the literature, demonstrating the effectiveness of the new method.

A Simple Hybrid Approach for the Synthesis of Heat Exchanger Networks Involving Internal Utility Exchangers

A Simple Hybrid Approach for the Synthesis of Heat Exchanger Networks Involving Internal Utility Exchangers

An anti-greedy random walk algorithm for heat exchanger network synthesis

Heat exchanger network (HEN) synthesis is a vibrant research field in process system engineering, with substantial contributions to energy conservation and emissions reduction initiatives. The optimal design of a heat exchanger network is not an easy task due to the abundance of local optima in the solution space caused by the non-linear, non-convex, and discontinuous nature of the problem. Generally, several heuristic algorithms employ a greedy evolutionary mechanism, optimize through greedily accepting the decrease in the objective function, and converge to obtain the optimal solution. The Random Walk algorithm has a simple evolutionary mechanism, is prone to mutation, and exhibits high flexibility. However, the algorithm's inherent persistent greediness in searching restrict the scope of the search. Thus, this paper proposes an anti-greedy concept based on the Random Walk method to serve as the basis of a new synthesis approach called the Anti-greedy Random Walk algorithm. Two strategies are proposed in the algorithm, which broaden the solution domain by slowing down rapid unit reduction and accepting imperfect solutions, respectively. One strategy is to thoroughly search for the integer and continuous variables of the HEN problem by covering a much larger search space. Another is to escape the local extrema and move forward to discover more possibilities. Quantitative data demonstrates the algorithm's ability to avoid the local extrema and enhance the search effectiveness. Three different scales of classical cases are used in this work and the obtained results are superior to the published ones.

A novel two-step approach for multi-plant indirect HENs design

Implementing interplant heat integration indirectly via intermediate fluid circuits is a reliable and promising means of enhancing the overall energy efficiency and reducing the carbon emissions of a chemical industrial zone. Although optimization-based sequential and simultaneous synthesis methods have been developed for interplant heat integration, obtaining optimal multi-plant indirect heat exchanger networks (HENs) remains a challenge due to the problem's size and complexity. Under this content, a two-step method is proposed based on the decomposable structural features of the HENs. In the first step, a mathematical model incorporating superstructure representation and pinch location technique is developed to optimize interplant intermediate stream allocation, by which the optimal configuration of intermediate fluids is determined by balancing utility targets and intermediate stream cost with pumps and pipelines. This approach enables multiple HENs synthesis to become independent, and in the second step, a compact MINLP model is proposed for the cost-optimal HEN design in each plant. The effectiveness and advantages of the proposed method are demonstrated by the better results obtained within reasonable computing times for three literature examples, particularly an industrial-scale problem.

Unlocking the potential of synthetic fuel production: Coupled optimization of heat exchanger network and operating parameters of a 1 MW power-to-liquid plant

The use of synthetic fuels is a promising way to reduce emissions significantly. To accelerate cost-effective large-scale synthetic fuel deployment, we optimize a novel 1 MW power-to-liquid (PtL) plant in terms of PtL-efficiency and fuel production costs. For numerous plants, the available waste heat and temperature level depend on the operating point. To optimize efficiency and costs, the choice of the operating point is included in the heat exchanger network synthesis. All nonlinearities are approximated using piecewise linear models and transferred to MILP. Adapting the epsilon constraint method allows us to solve the multi-criteria problem with uniformly distributed solutions on the Pareto front. We improved the lowest production costs of 1.89 €/kg and the highest efficiency of 58.08% from the conventional design process to 1.83 €/kg and 61.33%. By applying the presented method, climate-neutral synthetic fuels can be promoted and emissions can be reduced in the long term.

A comprehensive review of recent advancements and developments in heat exchanger network synthesis techniques

A comprehensive review of recent advancements and developments in heat exchanger network synthesis techniques

A novel mass–heat exchange network analogy, regression, and synthesis method for mass exchanger networks

Synthesis of mass exchanger network (MEN) is significant to save the material and reduce the pollution for process industries. Considering its small magnitude and concentration constraints, the optimized configuration of MEN can easily violate the concentration constraints, leading to a reduction in the structural diversity during the optimization process compared to that of a heat exchanger network (HEN) of the same size. Moreover, the constraint on the mass transfer driving force based on the equilibrium composition may also have major effects on the optimization process. To address these issues, this study presents a process–equipment–system analogy between the MEN and the HEN, proposing a novel mass–heat exchange network analogy, regression, and synthesis method. First, the generalized HEN was established by using an analogy considering the MEN based on either the original composition or the equilibrium composition. In this regard, a coordination coefficient was introduced to correct the deviation of the analogy relationship between the heat transfer and the mass transfer processes. Then, the generalized HEN was optimized by employing the random walk algorithm with compulsive evolution (RWCE). A regression method was proposed to regress the optimized HEN solution to the MEN expression, allowing the continuous optimization by using the RWCE with finer parameters. The final optimal solution obtained was found to be lower than those reported in the literature with enhanced structural diversity, demonstrating the effectiveness of the presented method and providing new ideas for further synchronous optimization of combined heat and mass exchanger networks.

A Flexible Heat Exchanger Network Synthesis Method Adapted to Multi-Operation Conditions

This paper presents a flexible HEN (heat exchanger network) synthesis methodology for designing a multiperiod HEN with streams involving phase changes. The methodology is based on an MINLP (mixed integer nonlinear programming) model, identification of critical points, and flexibility index analysis considering phase changes. A nominal multiperiod HEN topology is constructed in the first step. Then each process operating condition’s critical points and flexibility index are calculated to verify the feasibility of the designed HEN under multiple operating conditions. To verify the validity of the method, the proposed methodology will be applied to a case study on an ammonia synthesis process heat transfer network design based on renewable energy. The results show that the method can obtain a flexible heat transfer network that is cost-effective and adaptable to multi-condition production for green ammonia synthesis.

Formulating data-driven surrogate models for process optimization

Recent developments in data science and machine learning have inspired a new wave of research into data-driven modeling for mathematical optimization of process applications. This paper first considers essential conditions for robustness to uncertainties and accurate extrapolation, which are required to integrate surrogates into process optimization. Next we consider two perspectives for developing process engineering surrogates: a surrogate-led and a mathematical programming-led approach. As these data-driven surrogate models must be integrated into a larger process optimization problem, we discuss the verification problem, i.e., checking that the optimum of the surrogate corresponds to the optimum of the truth model. The paper investigates two case studies on surrogate-based optimization for heat exchanger network synthesis and drill scheduling.

A Tabu-Matching Heuristic Algorithm Based on Temperature Feasibility for Efficient Synthesis of Heat Exchanger Networks

The non-structural model of heat exchanger networks (HENs) offers a wide solution space for optimization due to the random matching of hot and cold streams. However, this stochastic matching can sometimes result in infeasible structures, leading to inefficient optimization. To address this issue, a tabu matching based on a heuristic algorithm for HENs is proposed. The proposed tabu-matching method involves three main steps: First, the critical temperature levels—high, medium, and low-temperature intervals—are determined based on the inlet and outlet temperatures of streams. Second, the number of nodes is set according to the temperature intervals. Third, the nodes of streams are flexibly matched within the tabu rules: the low-temperature interval of hot streams with the high-temperature interval of cold streams; the streams crossing cannot be matched. The results revealed that by incorporating the tabu rules and adjusting the number of nodes, the ratio of the feasible zone in the whole solution domain increases, and the calculation efficiency is enhanced. To evaluate the effectiveness of the method, three benchmark problems were studied. The obtained total annual costs (TACs) of these case studies exhibited a decrease of USD 4290/yr (case 1), USD 1435/yr (case 2), and USD 11,232/yr (case 3) compared to the best published results. The results demonstrate that the proposed tabu-matching heuristic algorithm is effective and robust.

Process design and improvement for hydrogen production based on thermodynamic analysis: Practical application to real-world on-site hydrogen refueling stations

An energy source transition is necessary to realize carbon neutrality, emphasizing the importance of a hydrogen economy. The transportation sector accounted for 27% of annual carbon emissions in 2019, highlighting the increasing importance of transitioning to hydrogen vehicles and establishing hydrogen refueling stations (HRSs). In particular, HRSs need to be prioritized for deploying hydrogen vehicles and developing hydrogen supply chains. Thus, research on HRS is important for achieving carbon neutrality in the transportation sector. In this study, we improved the efficiency and scaled up the capacity of an on-site HRS (based on steam methane reforming with a hydrogen production rate of 30 Nm3/h) in Seoul, Korea. This HRS was a prototype with low efficiency and capacity. Its efficiency was increased through thermodynamic analysis and heat exchanger network synthesis. Furthermore, the process was scaled up from 30 Nm3/h to 150 Nm3/h to meet future hydrogen demand. The results of exergy analysis indicated that the exergy destruction in the reforming reactor and heat exchanger accounted for 58.1% and 19.8%, respectively, of the total exergy destruction. Thus, the process was improved by modifying the heat exchanger network to reduce the exergy losses in these units. Consequently, the thermal and exergy efficiencies were increased from 75.7% to 78.6% and from 68.1% to 70.4%, respectively. The improved process was constructed and operated to demonstrate its performance. The operational and simulation data were similar, within the acceptable error ranges. This study provides guidelines for the design and installation of low-carbon on-site HRSs.

New robust approach for the globally optimal design of fired heaters

Fired heaters are important components of heat exchanger network (HEN) synthesis, especially in view of the existing procedures to simultaneously synthesize the HEN and obtain the basic design of heat exchangers. Existing basic design procedures for fired-heaters are either too simple, or difficult to solve when based on MINLP procedures. In addition, CFD approaches can hardly be used in design optimization and metaheuristics do not guarantee global optimality. We present two novel advances: a reformulation of models from previous optimization literature and we use Partial and Proxy Set Trimming followed by Smart Enumeration, a recently introduced robust technique. The proposed approach does neither needs initial solutions, nor does it have convergence problems and it also guarantees global optimality. The Set Trimming variant, namely Proxy Set Trimming, which was not part of the original Set Trimming version of Set Trimming is also used.

Synthesis and Retrofit of Multiperiod Heat Exchanger Networks Considering Heat Transfer Enhancement and Environmental Impact

Heat transfer enhancement has been used to achieve lower heat transfer area and reduced utility requirements in heat exchanger network synthesis problems involving retrofit. This paper presents a technique for implementing heat transfer enhancements in both grassroot and retrofit heat exchanger network synthesis problems involving multiperiod operations. For the grassroot scenario, the approach adopted involves extending the stage-wise superstructure model for multiperiod heat exchanger network synthesis to accommodate various options of tube-side and shell-side heat transfer enhancement techniques. The extended model is then applied to a retrofit scenario using the reduced superstructure synthesis approach. The results obtained demonstrate the benefits of heat transfer enhancement through reduced operating costs in the grassroot and retrofit scenarios. For the grassroot scenario, the operating and capital costs obtained in this paper are 12.3% and 20% lower than solutions obtained using conventional non-enhanced synthesis methods, while for the retrofit scenario, solutions which compare favourably with literature solutions, and which has the potential to be more environment friendly by virtue of lower energy utilisation, are obtained.