Heat Exchanger Network Research Articles

Modeling and design of a combined electrified steam methane reforming-pressure swing adsorption process

Steam methane reforming (SMR) is the most widely used hydrogen (H2) production method, converting natural gas and steam into H2 and carbon dioxide (CO2). SMR is a mature industrial technology that burns fossil fuels to provide heat to the endothermic reforming reaction and to generate steam, which contributes to the production of greenhouse gas emissions. In order to reduce heating-based emissions, an electrically-heated steam methane reforming process has been proposed. Conventional SMR uses a packed bed catalyst and receives heat through radiation from hot flames in the surrounding furnace; on the other hand, an electrified SMR employs a washcoated catalyst, and is resistively-heated through the wall of the reactor coil. To gain further insight into the scalability of hydrogen production processes using electrically-heated reformers, this paper takes experimental data from an electrified reformer built at UCLA, extracts kinetic parameters, and uses these parameters to model and scale up a hydrogen production plant with a hydrogen production capacity of 231 kg/h (2607 Nm3/h). The simulated plant includes a reformer, two water gas shift reactors, pressure swing adsorption (PSA) for separation, and a heat exchange network to make steam for the reformer. A two-column-PSA process is dynamically modeled to output 99% H2 purity, and the pressures necessary for separation and H2 recovery are calculated using steady-state simulation data. A sensitivity analysis is conducted for the most energy-efficient H2 production conditions (e.g., operating pressure, heat flux), and CO2 production amounts are compared to a conventional SMR process, demonstrating that electrified SMR can potentially be a significantly cleaner alternative. The optimization of electrified reformers must target high mass and heat transfer along the full length of the reformer to achieve near full methane conversion that will minimize off-gas generation from the PSA unit.

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.

Evaluation of dimethyl carbonate production process from CO2 by rigorous simulation and detailed optimization

AbstractThe CO2‐derived dimethyl carbonate (DMC) synthesis process becomes greatly attentive but suffers high energy consumption in DMC distillation process. In this work, the DMC‐MeOH azeotropes separation process by pressure swing distillation and extractive distillation was compared, and key operating parameters, including the total number of trays and the feeding position of the mixture liquid, were optimized with the minimum total annual cost (TAC) as the objective function. On the basis of this optimization, economic evaluation of different distillation processes was conducted, and it was found that extractive distillation was more economical than pressure swing distillation. The application of the dividing‐wall distillation process upgraded by extractive distillation can significantly reduce the minimum annual total cost by 37.4% and 10.7% compared to the original pressure swing distillation and extractive distillation process, respectively. The optimization of relevant heat exchange network based on pinch technology resulted in energy consumption reduction by 27.2% and 25.9% for its hot and cold utilities, respectively. Carbon life cycle assessment (LCA) on the DMC distillation process revealed over 50% of energy as well as carbon emissions from steam consumption, whose reduction can significantly minimize CO2 emissions, energy consumption, and ultimate cost. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Insight into Dynamic Characteristics of Concentrated Solar Power Systems with Calcium Looping Based on ZnO–Na2SO4 Co‐Doped CaO Composites

Concentrated solar power (CSP) integrated with calcium looping (CaL) technology has garnered significant interest as a solution to mitigate the issue of intermittency in solar power production. However, the deactivation of CaO‐based materials during cycling limits the application of CaL technology on a large scale. Herein, the dynamic characteristics of the CSP‐CaL system indirectly integrated with the s–CO2 Brayton cycle using CaO–ZnO–Na2SO4 composites are studied. Energy analysis shows that the energy storage density varies from 1328.13 to 1232.03 J g−1, and its average value increases by 32.0%. Round‐trip efficiency of the system varies from 40.0% to 39.5%, and its average value increases by 22.6%. Exergy analysis shows that the co‐doping of ZnO and Na2SO4 into CaO enhances the exergy efficiency of the calciner and the heat exchange network, but reduces the exergy efficiency of the carbonator. Exergy round‐trip efficiency of the CSP‐CaL system has slightly decreased from 44.0% to 43.4%, with an average increase of 22.8%. Techno‐economic analysis shows that the levelized cost of electricity for different sizes of CSP‐CaL systems using CaO–ZnO–Na2SO4 ranges from 176.44 to 153.70 $ MWh−1. Therefore, the CaO–ZnO–Na2SO4 composite shows promising prospects for application in solar thermal power generation.

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.

Enhancing the efficiency of power generation through the utilisation of LNG cold energy by a dual-fluid condensation rankine cycle system

As a clean energy source with high calorific value and low pollution, liquefied natural gas (LNG) has gained much attention and increased fast in the current energy market. It also has considerable cold energy resources that can be used to generate electricity during the regasification process. To fully utilise the cold energy of LNG, a double-Rankine cycle power generation system that incorporates heat exchange between LNG cold energy utilisation and a propane-ethylene cycle working medium is proposed and optimised. The optimisation is based on the Process Integration method, which uses Pinch Analysis to develop a Heat Exchange Network. Upon a specified LNG flow rate of 15.6 kg/s and natural gas delivery pressure of 7.85 MPaG, a retrofit case of the optimised LNG cold energy system generates a power of 1917.21 kW. A 28.6 % increase in power generation efficiency compared with the existing case. The result showed that by employing the Process Integration method, this study maximises the use of LNG cold energy through heat exchange with various working media, effectively addressing power generation efficiency issues. This approach is important in reducing power generation costs, minimising environmental impact, and advancing resource sustainability. Furthermore, it serves as a valuable reference for enhancing power generation efficiency by utilising LNG cold energy.

Study on the influence of coal on coal-to-ethylene glycol process considering the integration of reactor, distillation column, and heat exchanger network

The ethylene glycol (EG) process has low energy efficiency and high emissions. The raw coals purchased from different mines have different compositions and affect the reactor/column parameters and the integration of the heat exchanger network (HEN). An integrated reactor-distillation column-HEN model is built for optimizing the coal-to-ethylene glycol (CTEG) process considering the variation of raw coal. The particle swarm optimization (PSO) algorithm is used to solve this model and target the optimal raw coal for the CTEG process based on the interaction of MATLAB and Aspen Plus software. The influence of raw coal is obtained based on the sensitivity analysis performed for key reactor parameters and energy consumption. For the identified optimal raw coal, the mass fraction of carbon, hydrogen, oxygen, and ash are 84.02%, 6.80%, 1.00%, and 5.00%, respectively; the water added in the coal-water slurry is 62.15 t·h−1, and the water-gas ratio is 0.32, the corresponding energy consumption and the CO2 emission per unit product are 0.093 kgce⋅kmol−1 and 0.247 kgCO2⋅kmol−1, both decreased by 32.61%. The proposed method can be used to optimize the design and operation of the CTEG process and extended to optimize the other coal-based processes to achieve cleaner production.

Design of heat exchanger network physical layout in process plants using a mixed-integer model

Design of heat exchanger network physical layout in process plants using a mixed-integer model

A comprehensive framework for targeting the disturbance propagation path and debottleneck strategy of chemical process considering the topology and cascading effects

For the chemical processes, multiple fluctuations exist, and elucidating the system's dynamic changes is essential in improving its adaptability. With changes in reactor and separator operating conditions and cascading effects related to the system's topological structure considered, a comprehensive framework is established to target the disturbance propagation path and determine cost-effective debottlenecking strategies. Relations among distillation column parameters and the seasonal change in ambient temperature are deduced considering meteorological data, and the utility demand or column pressure under various conditions can be determined. All independent subsystems of the heat exchanger network and their heat surplus/deficit are identified based on the improved information flow diagram and output difference analysis, and the disturbance propagation path is determined. Correlations are deduced to clarify the heat transfer capacity requirement under fluctuating states, and the bottlenecks and cost-effective debottlenecking strategy without topology modification are identified at different stages. The proposed method is intuitive and efficient and can be applied in chemical processes' enhancement, retrofitting, and fault diagnosis. A benzene alkylation process is studied, with the total annual cost decreased by 3.4 %.

An Extended Superstructure-Based Model for Synthesis of Compression-Heat-Integrated Heat Exchanger Networks Coupled with Multiple Utilities

An Extended Superstructure-Based Model for Synthesis of Compression-Heat-Integrated Heat Exchanger Networks Coupled with Multiple Utilities

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.

A Formulated Method for Streams Splitting in Heat Exchanger Network Design Using Pinch Analysis

Abstract Thermal processes constitute a significant portion of energy consumption in the industrial sector. In this context, pinch analysis has emerged as a powerful method for achieving substantial energy savings. By systematically analyzing process streams and their heat transfer characteristics, pinch analysis enables the identification of heat recovery opportunities, leading to the design of an optimized heat exchanger network that minimizes energy requirements. In this study, a formulated stream splitting method is proposed to design a feasible minimum energy requirement heat exchanger network. This method aims to achieve two main goals. First, it gives a practical formulated method to help the designer when splitting streams and focuses on splitting the streams in such a way that creates sub-streams with the exact enthalpy required to satisfy heat exchanges with a specific number of streams, in order to minimize the need for process-utility heat exchangers whenever possible. Subsequently, the method aims to eliminate exergy destruction caused by temperature differences in the mixer used to recombine the split streams, by ensuring an isothermal mixture of streams, preventing unnecessary energy losses. The design of the heat exchanger network is conducted using the hint software, allowing for a comprehensive and detailed analysis of each step. The results obtained show that the heat exchanger network attained not only achieves the minimum energy consumption but also mitigates exergy destruction and avoids unnecessary process-utility heat exchangers, resulting in enhanced overall system performance.

Graphical pinch analysis-based method for heat exchanger networks retrofit of a residuum hydrogenation process

Sustainable energy systems are crucial for reducing carbon emissions because renewable energy sources leave a footprint. The petrochemical industry often suffers from inefficient heat exchange network (HEN) systems, leading to substantial energy wastage. In the current work, a real case study of the residue hydrogenation process was analyzed to identify potential energy savings. A new method combining Pinch Analysis and Thot–Tcold diagram analysis methods was proposed. This graphical analysis method plots the cold-flow temperature of each heat exchanger unit on the x-axis and the hot-flow temperature on the y-axis. By applying the Thot–Tcold diagram to a practical case of residue hydrogenation in Zhejiang, the existing process energy state was evaluated, and HEN was retrofitted to achieve energy savings and carbon emission reduction. Following optimization, the energy recovery amounted to 202.71 GJ/h with an energy recovery rate of 14.3 %. The proposed method saves approximately 4.058 × 105 GJ/y compared to current operations, resulting in an annual cost saving of approximately $ 2.76 M/y, with an investment payback period of less than 0.36 y. This study offers a solution to the energy challenges of industrial residue hydrogenation by enhancing the economic and environmental sustainability of existing process flows.

Practical exercises of computer-aided process synthesis for chemical engineering undergraduates

The study presents ten different exercises covering various computational tools. These exercises are practical applications presented to improve the understanding and skills of students in important concepts of chemical-aided process synthesis. A few exercises aim to build a foundation in computational techniques for chemical engineering undergraduates. The exercises are based on a spreadsheet that covers the design of regression analysis to find the optimum Antoine constants, array calculation for multicomponent distillation material balance, and the generation of a Gantt chart to plan and study the activities of batch processes. The other exercises included an introduction to process simulation, simulation, and reactor rating, and a simulation of multicomponent shortcut distillation. These exercises provide students with hands-on experience in utilizing process simulation software essential for analysing and optimizing chemical processes in real-world scenarios. The exercises also included the design of a heat exchanger network and solving a linear programming problem. An anonymous survey was collected from the cohort that had undergone the exercises, and the practical grades were compared with the batch that did not study the proposed exercises. Additionally, student feedback on practical exercises was collected. Based on the experience of the course coordinator and the collected feedback from participants, it was clear that the exercises helped students to inculcate critical thinking and self-learning abilities. An article essentially sheds light on the computer-aided practical exercises that enable chemical engineering graduates to engage in lifelong learning.

The neural network approach for estimation of heat transfer coefficient in heat exchangers considering the fouling formation dynamic

Routine maintenance for plate heat exchanger (PHE) cleaning improves the effectiveness of heat exchange network operation. Until recently, complex mathematical modelling was used to predict the value of the heat transfer coefficient after a certain period of operation of the heat exchanger, as well as the point in time when the coefficient reached the allowable limit. The applied mathematical tools included the systems of differential equations, matrices of heuristic coefficients, which needed a lot of computer resources. This paper offers an artificial neural network (ANN) technique for forecasting the following values: heat transfer coefficient at any time points during the operating period of PHEs; the time point, when the heat transfer coefficient reaches its lower permitted value. In this method, ANN uses the fuzzy logic techniques to expand the set of training parameters for the model, working with data of industrial measurements and data obtained from the mathematical modelling of the process. It allowed to train the developed feed-forward neural network (FFNN) with the coefficient of determination R2 equal to 0.99 and can predict the thermal resistance in PHE based on measurement data. To adequately predict the time-point to reach the limiting value of the heat transfer coefficient, it was proposed a recurrent neural network (RNN) with a hidden layer of long short-term memory (LSTM), where R2 value came up to 0.89.

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.

Simultaneous Integration of the Methanol-to-Olefin Separation Process and Heat Exchanger Network Based on Bi-Level Optimization

The separation section of the methanol-to-olefin (MTO) process is energy-intensive, and the optimization and heat integration can enhance energy efficiency and reduce costs. A bi-level optimization model framework is proposed to optimize the separation process and simultaneously integrate the heat exchanger network (HEN). The upper level employs a data-driven BP neural network proxy model instead of the mechanism model for the separation process, while the lower level adopts a stage-wise superstructure for the HEN without stream splits. The interaction between the two systems is realized effectively through information exchange. A bi-level particle swarm algorithm is employed to optimize complex problems and determine the optimal operational parameters for the distillation system and HEN. Compared with the typical sequential synthesis method, the optimization by the proposed approach reduces the total annual cost by 1.4293×106 USD/y, accounting for 4.76%.

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