Synthesis Of Networks Research Articles

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

Coarse-Grained Artificial Intelligence for Design of Brush Networks.

The ability to synthesize elastomeric materials with programmable mechanical properties is vital for advanced soft matter applications. Due to the inherent complexity of hierarchical structure-property correlations in brush-like polymer networks, the application of conventional theory-based, so-called Human Intelligence (HI) approaches becomes increasingly difficult. Herein we developed a design strategy based on synergistic combination of HI and AI tools which allows precise encoding of mechanical properties with three architectural parameters: degrees of polymerization (DP) of network strands, nx, side chains, nsc, backbone spacers between side chains, ng. Implementing a multilayer feedforward artificial neural network (ANN), we took advantage of model-predicted structure-property cross-correlations between coarse-grained system code including chemistry specific characteristics S = [l, v, b] defined by monomer projection length l and excluded volume v, Kuhn length b of bare backbone and side chains, and architecture A = [nsc, ng, nx] of polymer networks and their equilibrium mechanical properties P = [G, β] including the structural shear modulus G and firmness parameter β. The ANN was trained by minimizing the mean-square error with Bayesian regularization to avoid overfitting using a data set of experimental stress-deformation curves of networks with brush-like strands of poly(n-butyl acrylate), poly(isobutylene), and poly(dimethylsiloxane) having structural modulus G < 50 kPa and 0.01 ≤ β ≤ 0.3. The trained ANN predicts network mechanical properties with 95% confidence. The developed ANN was implemented for synthesis of model networks with identical mechanical properties but different chemistries of network strands.

Biomass RNA for the Controlled Synthesis of Degradable Networks by Radical Polymerization.

Nucleic acids extracted from biomass have emerged as sustainable and environmentally friendly building blocks for the fabrication of multifunctional materials. Until recently, the fabrication of biomass nucleic acid-based structures has been facilitated through simple crosslinking of biomass nucleic acids, which limits the possibility of material properties engineering. This study presents an approach to convert biomass RNA into an acrylic crosslinker through acyl imidazole chemistry. The number of acrylic moieties on RNA was engineered by varying the acylation conditions. The resulting RNA crosslinker can undergo radical copolymerization with various acrylic monomers, thereby offering a versatile route for creating materials with tunable properties (e.g., stiffness and hydrophobic characteristics). Further, reversible-deactivation radical polymerization methods, such as atom transfer radical polymerization (ATRP) and reversible addition-fragmentation chain transfer (RAFT), were also explored as additional approaches to engineer the hydrogel properties. The study also demonstrated the metallization of the biomass RNA-based material, thereby offering potential applications in enhancing electrical conductivity. Overall, this research expands the opportunities in biomass-based biomaterial fabrication, which allows tailored properties for diverse applications.

A Novel Chessboard Model for Optimization of Mass Exchange Network Synthesis

A Novel Chessboard Model for Optimization of Mass Exchange Network Synthesis

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.

Synthesis of flower-shaped cyclodextrin microporous organic networks for selective recovery of acetophenone and 1-phenylethanol from petrochemical effluents

Fabrication of efficient and renewable biomass-derivatives-based porous materials with large surface area, good stability and selectivity for recovering acetophenone (AP) and 1-phenylethanol (PE) from petrochemical by-products is of great significance and challenge. In this work, nine novel cyclodextrins-based microporous organic networks (CD-MONs) are designed and synthesized by choosing various CD-based building blocks and aromatic alkynes. Among them, a flower-shaped DEB-γ-CD-MON not only retains the cage-type cavity and the high density of hydroxyl sites within γ-CD, possesses the large surface area, outstanding chemical robustness and strong hydrophobicity, but also offers high AP adsorption capacity (1.93 mmol g−1), good AP/PE selectivity (8.1), and recycle stability both in batch and continuous adsorption modes. The experimental and theoretical results indicate that the hydrogen bonding, π-π stacking and van der Waals interaction between adsorbents and adsorbates are favorable for binding AP toward PE. This work not only provides a biomass-derivatives-based adsorbent for the separation and recovery of AP and PE from PO/SM waste effluent, but also breaks the limitation of the original structure and characteristic of natural biomass to realize added value in biomass valorization.

Problem solution of telecommunication networks parametric synthesis by the loop method

An algorithm for the parametric synthesis of telecommunication networks based on the criterion of minimizing the total throughput of communication channels is proposed. The problem is formulated in terms of linear programming. A feature of the proposed algorithm for obtaining a mathematical model is the use of the contour method of analysis that describes the distribution of information flows through flows in the fundamental cycles of the graph. A numerical example of the operation of the algorithm is given as well as an analysis of the results obtained.

Forensics of polymer networks.

Our lives cannot be imagined without polymer networks, which range widely, from synthetic rubber to biological tissues. Their properties-elasticity, strain-stiffening and stretchability-are controlled by a convolution of chemical composition, strand conformation and network topology. Yet, since the discovery of rubber vulcanization by Charles Goodyear in 1839, the internal organization of networks has remained a sealed 'black box'. While many studies show how network properties respond to topology variation, no method currently exists that would allow the decoding of the network structure from its properties. We address this problem by analysing networks' nonlinear responses to deformation to quantify their crosslink density, strand flexibility and fraction of stress-supporting strands. The decoded structural information enables the quality control of network synthesis, comparison of targeted to actual architecture and network classification according to the effectiveness of stress distribution. The developed forensic approach is a vital step in future implementation of artificial intelligence principles for soft matter design.

Strategic investment planning for the hydrogen economy – A mixed integer non-linear framework for the development and capacity expansion of hydrogen supply chain networks

This work presents a novel Mixed-Integer Nonlinear Programming (MINLP) modeling and optimization framework for the investment planning of large-scale hydrogen economy envelopes. The scope of the model includes the design, synthesis, and long-term capacity expansion of hydrogen supply chain networks (HSCN) that include hydrogen production, purification, storage, transportation, and distribution, subject to environmental sustainability considerations. Production and carbon capture units are represented using surrogate models derived from rigorous simulations to capture important process non-linearities. This framework provides an optimal roadmap for the 10-year plan of hydrogen infrastructure development in the state of California, that demonstrates the important trade-offs between investment decisions, economic incentives, and regulatory carbon emissions constraints. It is shown that the development of the hydrogen production and distribution networks can be achieved over a 10-year horizon in an economically profitable way, while achieving a 90 % reduction in carbon emissions and satisfying all state regulatory mandates.

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.

Electrically-driven textiles using hierarchical aramid fiber

High strength and flexible electronic textiles show promise in aerospace, robotics, and extreme environment applications, while it is indispensable to develop functionally integrated fiber building blocks with durable materials performance. Here we report multifunctional sensory e-textiles driven by using copper-grafted polyaramid (Kevlar®) fiber networks for the integrated electromagnetic interference shielding, thermal and pressure sensing. The hierarchical structural design and synthesis of flexible conductive networks is accomplished via the synergistic coordination between molecular copper complex and Kevlar® fibers, which facilitate long-term cyclability, stability, and washability. Superior Joule heating capabilities with a rapid thermal response under a low driving voltage (with a heating rate of 6 ºC/s and a cooling-down rate of 5.8 ºC/s), together with a high level of EMI shielding (71 dB from 8 to 13 GHz), resulting from its multilayered, vertically-aligned structure with continuous percolation of conductive coating. This work presents a new and feasible grafting approach for the large-scale fabrication of multifunctional wearable e-textiles.

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.

Optimal Pathways for Nitric Acid Synthesis Using P-Graph Attainable Region Technique (PART)

Developing a chemical reaction network is considered the first and most crucial step of process synthesis. Many methods have been employed for process synthesis, such as the attainable region (AR) theory. AR states that a region of all possible configurations can be defined with all the potential products and reactants. The second method is process network synthesis (PNS), a technique used to optimise a flowsheet based on the feasible materials and energy flow. P-graph is an algorithmic framework for PNS problems. P-graph attainable region technique (PART) is introduced here as an integration of both AR and P-graph to generate optimal reaction pathways for a given process. A descriptive AR plot is also developed to represent all the possible solution structures or reaction pathways. A case study of a conventional nitric acid synthesis process was used to demonstrate this technique.

MARKOV NETWORK MODEL

Markov networks synthesized from the N-scheme of the Markov algorithm, developed to replace his well-known y-scheme, are studied theoretically from the standpoint of theories of algorithms and categories. The provisions of the theory of Markov algorithms are developed in the direction of synthesizing methods for the joint processing of words and morphism values that are occurrences in these words. In Markov networks, morphisms are weighted according to the method adopted in artificial neural networks. The material for the synthesis of Markov networks are diagrams of Markov occurrences of words into each other, obtained from input data samples, by classifying occurrences in the alphabet M = λιτφпо1…оx/αβδγχκμ. The Markov network model and the activation function of its morphemes are synthesized and subjected to scientific analysis. The idea of joint consideration of category theory and the theory of Markov algorithms is developed for the control channel, so this control channel is understood as an n-category associator introduced into the N-scheme of the Markov algorithm. In Markov networks, chain termination mechanisms are studied, using the control channel command, using combined methods of classifying words and morphisms, previously studied for the N-scheme of his algorithm, the evolution of categories formed by words is considered due to their occurrences in each other. For the method of synthesizing training samples from diagrams of Markov occurrences, a scientific substantiation of prescriptions for processing samples is carried out. For activation functions, examples of their implementation are considered. The categorical representation of the activation function is synthesized for Markov networks and subjected to a separate scientific analysis. For single-layer and multi-layer Markov networks of direct distribution, a block diagram has been developed. In Markov networks, the concept of zero-nets is introduced. As a promising direction for further theoretical research in Markov networks, multilayer Markov networks of indirect propagation are singled out, which have a conclusion that cannot be considered normal according to Markov. In the conclusions to the study, it is noted that Markov networks have a category-theoretic representation and can be synthesized from Markov zero-nets.

A review of practice of using evolutionary algorithms for neural network synthesis and training

The object of this research is the application of evolutionary algorithms for the synthesis and training of neural networks. The paper aims to select and review the existing experience on using evolutionary algorithms as competitive methods to conventional approaches in neural network training and creation, and to evaluate such existing solutions for further development of this field. The essence of the obtained results lies in the successful application of genetic algorithms in conjunction with neural networks to optimize parameters, architecture, and weight coefficients of the networks. The genetic algorithms allowed improving the performance and accuracy of neural networks, especially in cases where backpropagation algorithms faced difficulties in finding optimal solutions. These results can be attributed to the fact that genetic algorithms are efficient methods for global optimization in parameter space. They help avoid local minima and discover more reliable and stable solutions. The obtained findings can be practically utilized to enhance the performance and quality of neural networks in various classification and prediction tasks. The use of genetic algorithms enables the selection of optimal weight coefficients, network connections, and identification of significant features from the dataset. However, they come with the limitation of additional time costs for evaluating the entire population according to the selection criteria. It is worth noting that the application of genetic algorithms is not a universal method for all tasks, and the algorithm parameters should be individually tuned for each specific problem. Further research could focus on refining the combination methods of genetic algorithms and neural networks, as well as exploring their application in new domains and tasks.

Automatically Finding the Right Probabilities in Bayesian Networks

This paper presents alternative techniques for inference on classical Bayesian networks in which all probabilities are fixed, and for synthesis problems when conditional probability tables (CPTs) in such networks contain symbolic parameters rather than concrete probabilities. The key idea is to exploit probabilistic model checking as well as its recent extension to parameter synthesis techniques for parametric Markov chains. To enable this, the Bayesian networks are transformed into Markov chains and their objectives are mapped onto probabilistic temporal logic formulas. For exact inference, we compare probabilistic model checking to weighted model counting on various Bayesian network benchmarks. We contrast symbolic model checking using multi-terminal binary (aka: algebraic) decision diagrams to symbolic inference using proba- bilistic sentential decision diagrams, symbolic data structures that are tailored to Bayesian networks. For the parametric setting, we describe how our techniques can be used for various synthesis problems such as computing sensitivity functions (and values), simple and difference parameter tuning and ratio parameter tuning. Our parameter synthesis techniques are applicable to arbitrarily many, possibly dependent, parameters that may occur in multiple CPTs. This lifts restrictions, e.g., on the number of parametrized CPTs, or on parameter dependencies between several CPTs, that exist in the literature. Experiments on several benchmarks show that our parameter synthesis techniques can treat parameter synthesis for Bayesian networks (with hundreds of unknown parameters) that are out of reach for existing techniques.

Towards achieving SDG-6 in steel industry: A superstructure optimization-based approach

The increasing water scarcity and stringent norms on environmental pollution control impose a demand on the development of sustainable methods for water use in process operations. The steel industry is one such highly water-intensive industry with a water requirement of 2–4 m3/ton of steel and a production of 1950 million tons in 2021 globally. Optimum water network synthesis by recycling, regeneration, and reuse of water, is one of the ways of sustainable management of water in industries. This study explores several opportunities for water network optimization in the steel industry, through appropriate water flow targeting. The opportunities for direct reuse of blowdown water, or water reuse after regeneration of blowdown and process loss water as makeup water, are explored. These opportunities are evaluated algorithmically using a superstructure-based optimization framework. Realistic operational constraints such as the hardness of makeup water and the Langelier Saturation Index (LSI) value of blowdown water are also incorporated in the developed formulation. The interplay between the cycles of concentration (CoC) and blowdown water quality is also studied, first with a representative example problem and then on a representative steel complex network. The advantages of inter-plant water reuse over intra-plant water reuse and the effect of pumping cost on the optimized water network are demonstrated. The result shows that operating cost and freshwater requirement can be reduced to around 50 % by inter-plant reuse and regeneration of blowdown and process loss water through appropriate recycle streams, and by optimizing the operating CoC of the cooling towers in the cooling systems. It is also observed that the cooling towers should be operated locally at the resultant optimal CoC as compared to the conventional method of operating all at the highest possible blowdown concentration to achieve maximum freshwater savings in the water system.

Synthesis and Applications of Hybrid Polymer Networks Based on Renewable Natural Macromolecules.

Macromolecules obtained from renewable natural sources are gaining increasing attention as components for a vast variety of sustainable polymer-based materials. Natural raw materials can facilitate continuous-flow production due to their year-round availability and short replenishment period. They also open new opportunities for chemists and biologists to design and create "bioreplacement" and "bioadvantaged" polymers, where complex structures produced by nature are being modified, upgraded, and utilized to create novel materials. Bio-based macromonomers are expected not only to compete with but to replace some petroleum-based analogs, as well. The development of novel sustainable materials is an ongoing and very dynamic process. There are multiple strategies for transforming natural macromolecules into sophisticated value-added products. Some methods include chemical modification of macromolecules, while others include blending several components into one new system. One of the most promising approaches for incorporating renewable macromolecules into new products is the synthesis of hybrid networks based on one or more natural components. Each one has unique characteristics, so its incorporation into a network brings new sustainable materials with properties that can be tuned according to their end-use. This article reviews the current state-of-the-art and future potential of renewable natural macromolecules as sustainable building blocks for the synthesis and use of hybrid polymer networks. The most recent advancements and applications that involve polymers, such as cellulose, chitin, alginic acid, gellan gum, lignin, and their derivatives, are discussed.