Efficient separation method for preparing n-alkane phase change material from coal-based oil

Abstract

The invention relates to the technical field of automatic control or adjustment, and further relates to an efficient separation method for preparing an n-alkane phase change material from coal-based oil. The method comprises the following steps: 1, pretreating coal-based oil, decomposing components of the coal-based oil by utilizing a multi-stage pyrolysis reaction, and calculating the concentrations of various products; 2, inputting the concentration of each product into the optimization model; the optimization model optimizes the reaction temperature and the reaction pressure of the separation tower to obtain an optimized reaction temperature and an optimized reaction pressure; 3, based on the optimized reaction temperature and the optimized reaction pressure, solvent selection, extraction temperature and extraction pressure in the supercritical fluid extraction process are optimized, and the extraction efficiency is maximized. The separation conditions are optimized through the technologies such as the multi-stage pyrolytic reaction, the Langmuir adsorption model, the supercritical fluid extraction and the particle swarm optimization algorithm, the separation efficiency and the purity of the n-alkanes are improved, and the energy consumption is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of automatic control or regulation, and in particular relates to a high-efficiency separation method for preparing normal alkane phase change materials from coal-based oil. Background Art

[0002] With the increasing global energy demand and the urgent need for environmental protection, the efficient use and storage of energy has become a key area in scientific research and industrial production. Phase change materials, as a material that can store and release a large amount of latent heat through phase change processes, have been widely used in energy storage, building energy conservation, electronic heat dissipation and other fields in recent years. Phase change materials have the characteristics of absorbing or releasing a large amount of energy through solid-liquid, liquid-gas, solid-gas phase changes, etc., and they show excellent energy storage capacity during temperature changes. Therefore, they are considered to be an important part of future energy management systems. Among these phase change materials, alkane compounds, especially normal alkanes, have gradually become one of the research hotspots because of their high phase change latent heat, relatively stable phase change temperature and good chemical stability.

[0003] In the prior art, the preparation and separation of normal alkanes are mainly completed through processes such as cracking and distillation of petroleum or natural gas-based compounds. However, due to the limited petroleum resources and the complex and high energy consumption of traditional separation processes, how to efficiently prepare and separate high-purity normal alkanes through other low-cost raw materials has become a problem that technicians urgently need to solve. In this context, coal-based oil, as a raw material containing a large amount of alkane compounds, has gradually become a potential source for the preparation of normal alkanes because of its abundant resources and low price. Coal-based oil is a complex mixture generated by coal chemical processes, containing a large amount of aromatic hydrocarbons, normal alkanes and other hydrocarbons, and has a high application potential. However, due to the complex composition of coal-based oil and the large amount of impurities it contains, the prior art still faces many challenges when separating high-purity normal alkanes from it. Summary of the invention

[0004] The main purpose of the present invention is to provide an efficient separation method for preparing normal alkane phase change materials from coal-based oil. The present invention optimizes separation conditions through multi-stage pyrolysis reaction, Langmuir adsorption model, supercritical fluid extraction and particle swarm optimization algorithm, thereby improving the separation efficiency and purity of normal alkanes and reducing energy consumption.

[0005] In order to solve the above problems, the technical solution of the present invention is achieved as follows:

[0006] An efficient separation method for preparing normal alkane phase change materials from coal-based oil, the method comprising:

[0007] Step 1: pre-treat the coal-based oil, set the reaction temperature and reaction pressure of the separation tower, decompose the components of the coal-based oil by multi-stage pyrolysis reaction, obtain various products, and calculate the concentration of various products;

[0008] Step 2: Input the concentration of each product into the optimization model; the optimization model optimizes the reaction temperature and reaction pressure of the separation tower to maximize the separation efficiency of normal alkanes, and obtains the optimized reaction temperature and the optimized reaction pressure;

[0009] Step 3: Based on the optimized reaction temperature and the optimized reaction pressure, the adsorption rate of n-alkanes in the molecular sieve is calculated by the Langmuir adsorption model; the adsorption rate is input into the particle swarm optimization algorithm model to optimize the solvent selection, extraction temperature and extraction pressure in the supercritical fluid extraction process to maximize the extraction efficiency.

[0010] Furthermore, the concentrations of various products were calculated by solving the following equations:

[0011]

[0012] Among them, C i is the concentration of the i-th product at time t; is the concentration of the jth product at the initial moment, that is, the input concentration at the beginning of the reaction; k i，j is the reaction rate constant for the conversion of the jth product to the ith product; E i，j is the activation energy of the conversion of the jth product into the ith product; T is the reaction temperature; R is the gas constant; C i，max is the maximum concentration of the ith product at equilibrium; t is the reaction time; is the standard enthalpy change when the concentration of the ith product changes; is the standard entropy change when the concentration of the ith product changes; P is the reaction pressure.

[0013] Furthermore, the optimization model is a genetic algorithm model; when the genetic algorithm model optimizes the reaction temperature T and the reaction pressure P of the separation tower, its fitness function F GA (T, P) is expressed using the following formula:

[0014]

[0015] Where m is the number of products; n is the number of normal alkane products; C k (T, P) is the concentration of the kth normal alkane product at reaction temperature T and reaction pressure P, C total (T, P) is the total concentration of all products, λ is a balance weight factor; C l(T, P) is the concentration of the first non-normal alkane product at reaction temperature T and reaction pressure P; through iterative optimization of the genetic algorithm model, a set of optimal reaction temperatures T is finally found. opt and the optimal reaction pressure P opt , so that the separation efficiency of normal alkanes reaches the maximum value:

[0016] (T opt , P opt )=argmaxF GA (T, P);

[0017] The optimal reaction temperature T opt As the optimal reaction temperature and the optimal reaction pressure P opt As the optimized reaction pressure.

[0018] Furthermore, the adsorption rate θ of the kth normal alkane product in the molecular sieve is calculated by the Langmuir adsorption model. k The formula is:

[0019]

[0020] Among them, K Topt The normal alkane products are at the optimized reaction temperature T opt The adsorption constant under .

[0021] Furthermore, the objective function of the particle swarm optimization algorithm model in step 3 is defined as:

[0022]

[0023] Among them, T s opt is the extraction temperature in the optimized supercritical fluid extraction process; is the extraction pressure in the optimized supercritical fluid extraction process; S opt Solvent selection for optimized supercritical fluid extraction process.

[0024] Furthermore, E PSO (T s , P s , S) is the optimization function of the particle swarm optimization algorithm model, which is defined as:

[0025]

[0026] Among them, S k (T s , P s ) is in T s and P s The solubility of solvent S for the kth normal alkane product under the conditions; θ totalis the total adsorption rate of all n-alkane products; C l (T s , P s ) is the first non-normal alkane product in T and P s The concentration under the condition; C feed is the total concentration of all products in the feed entering the extraction process; n is the total number of normal alkane products.

[0027] Furthermore, the method also includes: Step 4: Based on the optimized extraction temperature, solvent selection and extraction pressure in the supercritical fluid extraction process, the Peng-Robinson equation is used to describe the gas-liquid equilibrium of the normal alkane to calculate the compression factor, and the phase separation efficiency of the normal alkane is calculated through the compression factor; based on the phase separation efficiency of the normal alkane, the energy storage efficiency of the normal alkane as a phase change material is calculated.

[0028] Furthermore, the compression factor is calculated by the following formula:

[0029]

[0030] Where Z is the compression factor;

[0031] The phase separation efficiency of normal alkanes is calculated by the compression factor using the following formula:

[0032]

[0033] Among them, P range is the pressure range; E k is the phase separation efficiency of the kth n-alkane product; is the vapor-liquid equilibrium concentration of the kth normal alkane product under the compression factor Z; is the concentration of the kth normal alkane product in the feed; A is the pre-crystallization factor; ΔG / is the critical free energy barrier; C is the supersaturation; S c is the critical supersaturation; B is the Boltzmann constant; T inf is the absolute temperature.

[0034] Furthermore, the energy storage efficiency of normal alkanes as phase change materials is calculated by the following formula:

[0035]

[0036] Among them, m PCM is the mass of the phase change material; ΔH f is the latent heat of phase change; T min is the minimum allowable temperature; T max is the maximum allowable temperature; C p (T X ) is the phase transition temperature TX Specific heat capacity of phase change material under .

[0037] The efficient separation method for preparing normal alkane phase change material from coal-based oil of the present invention has the following beneficial effects:

[0038] The present invention adopts the technology of multi-stage pyrolysis reaction in the process of separating normal alkanes, which greatly improves the separation efficiency. It is usually difficult to accurately control the reaction conditions of traditional single-stage pyrolysis reactions, resulting in complex products and great difficulty in separation. In contrast, the multi-stage pyrolysis reaction in the present invention achieves the selective decomposition of different components in coal-based oil by gradually regulating the reaction temperature and pressure. Especially at a lower reaction temperature, the secondary cracking of normal alkanes can be effectively avoided, so that the amount of normal alkanes generated is maximized. At the same time, by controlling the reaction conditions in multiple stages, the generation of by-products is reduced, and the complexity of subsequent separation steps is further reduced. Therefore, the present invention makes the separation of normal alkanes more efficient and reduces energy consumption by optimizing the pyrolysis reaction conditions. This technology greatly improves the feasibility of industrial production.

[0039] Another significant advantage of the present invention is the introduction of supercritical fluid extraction technology, which achieves comprehensive optimization of the extraction process by combining with the particle swarm optimization algorithm. Supercritical fluid extraction technology is widely used in the separation process of complex compounds because it can separate target substances under efficient and mild conditions. However, traditional supercritical fluid extraction relies on experience or experimental parameters, and it is difficult to accurately control the optimal extraction temperature, pressure and solvent selection. The present invention uses the particle swarm optimization algorithm to perform intelligent iterative optimization of these parameters and finds the optimal extraction conditions that can maximize the separation efficiency of normal alkanes. Compared with traditional methods, the particle swarm optimization algorithm shows a strong global search capability when dealing with multivariable optimization problems, avoiding the possibility of falling into local optimality. Through the application of this optimization technology, the present invention can find the optimal operating conditions in the shortest time, greatly improve the extraction efficiency, and reduce the waste of solvents and energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic diagram of a method flow chart of an efficient separation method for preparing normal alkane phase change material from coal-based oil provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0042] Example 1, reference Figure 1 : An efficient separation method for preparing normal alkane phase change materials from coal-based oil, the method comprising:

[0043] Step 1: pre-treat the coal-based oil, set the reaction temperature and reaction pressure of the separation tower, decompose the components of the coal-based oil by multi-stage pyrolysis reaction, obtain various products, and calculate the concentration of various products;

[0044] Coal-based oil is an extremely complex fossil fuel, which contains many different types of organic compounds, such as aromatic hydrocarbons, normal alkanes, cycloalkanes, etc. In the traditional single-stage pyrolysis reaction, it is usually difficult to accurately control the cracking behavior of these different components, resulting in complex and low-purity separated products, especially when extracting normal alkanes, it is difficult to obtain high-purity target products. Therefore, the present invention gradually decomposes the compounds in coal-based oil by means of a multi-stage pyrolysis reaction to improve the separation efficiency of normal alkanes. The core of the multi-stage pyrolysis reaction is to selectively decompose different components in coal-based oil using different temperature and pressure conditions. In each stage of the pyrolysis reaction, controlling the specific temperature and pressure can cause some relatively stable long-chain alkane molecules to crack into shorter-chain normal alkanes, while other complex molecular structures, such as aromatic hydrocarbons, crack at different temperature stages. Through this step-by-step reaction process, the secondary cracking of normal alkanes by excessively high temperatures is avoided, and the generation of impurities can be effectively reduced. This multi-stage control method can achieve higher selectivity, making the separation process of normal alkanes more accurate. By controlling the temperature and pressure conditions at each stage, normal alkanes can be separated and enriched under milder conditions, reducing unnecessary energy consumption, while also lowering the requirements for equipment and improving the economy of the entire process.

[0045] During the reaction process, the components of the coal-based oil change dynamically with the passage of reaction time, and the concentration of each compound is different at different time points. These concentration changes directly reflect the pyrolysis behavior and decomposition rate of each component in the coal-based oil. In the present invention, the reaction system can monitor the material distribution in the separation process in real time by accurately calculating the product concentration at different time points. As time goes by, the concentration of normal alkanes gradually increases, while the concentration of other impurities gradually decreases due to their different cracking temperatures or different molecular structures. This concentration change over time provides a data basis for subsequent optimization. By collecting multi-point data, it can be clarified which temperature and pressure conditions are most conducive to the generation and enrichment of normal alkanes in different time periods. In addition, the setting of the temperature and pressure in the reaction tower has an important influence on the reaction process. Too high a temperature may cause further cracking of normal alkanes, generate more gaseous products, and lead to a decrease in separation efficiency; too low a pressure may reduce the concentration of the product, making the subsequent separation step more difficult. Therefore, in this step, the temperature and pressure in the reaction tower need to be accurately controlled according to the characteristics of the coal-based oil and the reaction characteristics of different components. At different temperatures and pressures, the volatility and thermal stability of normal alkanes and other components vary, which is the fundamental reason why multi-stage pyrolysis reactions can achieve efficient separation. Compared with the existing single-stage reaction process, the multi-stage pyrolysis reaction of the present invention has significant innovation and advantages. Traditional single-stage pyrolysis often cannot effectively distinguish the complex components in coal-based oil, resulting in low separation purity. The multi-stage reaction can maximize the retention of the structural integrity of normal alkanes by controlling the reaction conditions in stages, thereby avoiding further cracking at high temperatures.

[0046] Step 2: Input the concentration of each product into the optimization model; the optimization model optimizes the reaction temperature and reaction pressure of the separation tower to maximize the separation efficiency of normal alkanes, and obtains the optimized reaction temperature and the optimized reaction pressure;

[0047] During the pyrolysis process, various components in coal-based oil will be gradually decomposed over time, and the concentration data of these components at various time points can reflect the distribution and conversion of substances under different reaction conditions. By collecting these concentration data and inputting them into the optimization model, the model can accurately capture the generation trend of n-alkanes and the reduction trend of impurities. The goal of the optimization model is to find the optimal temperature and pressure combination to maximize the concentration of n-alkanes while minimizing other unwanted byproducts. By adjusting these parameters, the model can not only increase the output of n-alkanes, but also reduce the burden on subsequent extraction steps. The core idea of ​​the optimization model is based on the generation mechanism of n-alkanes and the difference in thermal stability between n-alkanes and other compounds under different conditions. The n-alkanes in coal-based oil can maintain their chain structure under moderate temperature and pressure conditions, avoiding cracking into small molecules or gaseous hydrocarbons due to excessive pyrolysis. Under inappropriate temperature or pressure, n-alkanes may undergo secondary cracking to generate more complex or difficult-to-separate products. Therefore, the optimization model can find the optimal parameters within a narrow temperature and pressure window by continuously iterating and adjusting the reaction conditions to ensure the selective separation of n-alkanes. During this process, the model also takes into account the pyrolysis behavior of other components in coal-based oil to avoid increasing the generation of other impurities while increasing the yield of normal alkanes, which would increase the separation complexity.

[0048] Compared with the traditional empirical method or single factor adjustment method, the advantage of the optimization model lies in its systematicity and automation. Traditional methods usually rely on repeated experiments to explore the optimal conditions by gradually adjusting the temperature or pressure. This method is not only time-consuming and labor-intensive, but also difficult to accurately find the optimal point. The optimization model can quickly process a large amount of concentration data through mathematical modeling and calculation methods. Based on the trend analysis of these data, it can quickly converge to the optimal solution, reducing the experimental cost and time consumption. In addition, the introduction of the model makes this process more repeatable and accurate, avoiding possible errors in manual operation. Furthermore, the optimization model not only optimizes the separation of n-alkanes, but also provides a guarantee for the operating efficiency of the entire system. By setting a specific objective function in the model, the adjustment of reaction conditions is not limited to the generation of n-alkanes, but also includes the control of energy consumption, the regulation of reaction speed, and the consideration of equipment safety. For example, the optimization goal set in the model can comprehensively consider the balance between n-alkanes production and energy consumption, so as to reduce operating costs while ensuring efficient separation. With the progress of optimization iterations, the model can continuously approach the optimal reaction temperature and pressure, so that the entire system can reach the best operating state. Compared with the prior art, the innovation of the present invention is that the separation process is precisely controlled through an intelligent optimization model. Existing separation technologies usually rely on the setting and adjustment of empirical parameters, and it is difficult to achieve real-time optimal control. The optimization model in the present invention can analyze product concentration data in real time and dynamically adjust the reaction conditions according to the changing trends of these data. This intelligent optimization process greatly improves the accuracy and efficiency of separation, not only improves the yield of normal alkanes, but also greatly reduces the workload of subsequent separation and purification. In addition, the automation characteristics of the model also make it easier to promote and apply in industrial production, especially in large-scale continuous production equipment, which can achieve unmanned or low-human intervention efficient operation.

[0049] Step 3: Based on the optimized reaction temperature and the optimized reaction pressure, the adsorption rate of n-alkanes in the molecular sieve is calculated by the Langmuir adsorption model; the adsorption rate is input into the particle swarm optimization algorithm model to optimize the solvent selection, extraction temperature and extraction pressure in the supercritical fluid extraction process to maximize the extraction efficiency.

[0050] Under the optimal reaction conditions obtained by the optimization model, the concentration difference between n-alkanes and other impurities has been basically established, but to further achieve the effective separation of high-purity n-alkanes, it is necessary to use the Langmuir adsorption model to calculate the adsorption rate of n-alkanes in molecular sieves. The Langmuir adsorption model was introduced because it can accurately describe the adsorption behavior of gas or liquid molecules on solid surfaces (such as molecular sieves). In the separation process of coal-based oil, molecular sieve materials have selective adsorption characteristics and can preferentially adsorb n-alkanes rather than other complex compounds. The Langmuir model can help quantitatively predict the adsorption rate and efficiency of n-alkanes under specific conditions by setting reasonable temperature, pressure and adsorption parameters. This lays a solid theoretical foundation for the subsequent separation and extraction process. After the adsorption rate of n-alkanes is calculated by the Langmuir adsorption model, the next goal is to further optimize the process parameters of supercritical fluid extraction. Supercritical fluid extraction is an efficient separation technology that uses the unique solubility properties of fluids in a supercritical state to achieve selective extraction of target substances. Here, the selection of supercritical fluid solvent, extraction temperature and extraction pressure are the key factors affecting the extraction efficiency of n-alkanes. Due to the differences in the physical and chemical properties of n-alkanes and other impurities, appropriate solvents and operating conditions can make n-alkanes easier to extract, while other impurities are effectively excluded. However, how to accurately find the optimal extraction conditions is a complex multi-parameter optimization problem, which is where the particle swarm optimization algorithm comes into play.

[0051] Particle swarm optimization (PSO) is an optimization method based on swarm intelligence that simulates the collaborative behavior of birds or fish flocks when searching for food. Each "particle" represents a possible combination of solvent, temperature and pressure. The particle swarm continuously adjusts its "position" by simulating the search behavior of the group to find the best extraction conditions. For the extraction process of n-alkanes, the advantages of PSO lie in its fast convergence and powerful global search capabilities. By inputting the results of the Langmuir adsorption model as the initial conditions, the particle swarm optimization algorithm can automatically explore a large number of possible combinations and find the parameter set that is most conducive to the extraction of n-alkanes. In this process, the algorithm will comprehensively consider multiple factors, such as the polarity of the solvent, the solubility of n-alkanes, the effect of extraction pressure on intermolecular interactions, and the regulating effect of temperature on fluid properties and extraction efficiency. Each particle continuously updates its speed and position, so that the entire particle swarm gradually converges to the global optimal solution in the search space, thereby maximizing the extraction efficiency of n-alkanes. This extraction optimization method based on mathematical models and intelligent algorithms not only far exceeds the traditional trial and error method in efficiency, but also reduces unnecessary energy consumption and solvent waste in the extraction process. The automation and intelligence of this process enables the entire separation process to be stably operated without human supervision or low intervention, greatly improving production efficiency. At the same time, due to the good robustness of the particle swarm optimization algorithm, it can adapt to the changes caused by the fluctuation of components in different batches of coal-based oil, making the entire separation process more stable and reliable. Compared with the existing supercritical extraction technology, the innovation of the present invention lies in the organic combination of the Langmuir adsorption model and the particle swarm optimization algorithm, thereby achieving precise control of normal alkanes at the molecular level. The Langmuir model provides a theoretical prediction of the adsorption process, while the particle swarm optimization comprehensively optimizes the multi-parameter extraction process through an intelligent algorithm. This combination not only improves the extraction efficiency of normal alkanes, but also reduces the residual impurities, greatly improving the purity of normal alkanes. In addition, the traditional supercritical extraction process usually relies on empirical parameters, and it is difficult to optimize the process conditions within a large range. The present invention uses intelligent optimization methods to make the separation process more efficient and stable, and has significant industrial application value.

[0052] Example 2: The concentrations of various products were calculated by solving the following equations:

[0053]

[0054] Among them, C i is the concentration of the ith product; is the concentration of the jth product at the initial moment, that is, the input concentration at the beginning of the reaction; k i，j is the reaction rate constant for the conversion of the jth product to the ith product; E i，jis the activation energy of the conversion of the jth product into the ith product; T is the reaction temperature; R is the gas constant; C i，max is the maximum concentration of the ith product at equilibrium; t is the reaction time; is the standard enthalpy change when the concentration of the ith product changes; is the standard entropy change when the concentration of the ith product changes; P is the reaction pressure.

[0055] Specifically, the left side of the equation Represents the concentration of the i-th product C i The rate of change over time t is the most important dynamic variable in the separation process because it directly affects how to control the system's temperature, pressure and other external conditions to achieve the efficient production of the target product, normal alkanes. The sum part on the right side of the equation reflects the specific transformation process of various chemical reactions. Since coal-based oil contains a variety of different compounds, they will be converted into other compounds during the reaction. Therefore, each term in the sum represents a specific transformation process, that is, the reaction path from the jth compound to the i-th compound. The core element here is the reaction rate constant k i，j and the initial concentration These two factors together determine the rate at which different compounds are generated in the initial stage of the reaction. In particular, the reaction rate constant k i，j describes the rate at which the jth product is converted to the ith product at a specific temperature, while the initial concentration It provides the amount of substance at the beginning of the reaction, which directly determines the initial conditions of the reaction. The role of temperature T in the formula is particularly critical. It is expressed through the Arrhenius equation The reaction rate is exponentially affected. Under high temperature conditions, the reaction rate is accelerated, which means that the molecules in the coal-based oil will be more likely to break through the activation energy barrier and be converted into the target product normal alkanes or other intermediates. On the contrary, too low a temperature may cause the reaction rate to be too slow and fail to effectively generate enough normal alkanes. Therefore, reasonable temperature adjustment is the key to ensure the smooth progress of the separation process. In addition, the activation energy E in the formula i，j It reflects the energy required to transform the jth product into the i-th product.

[0056] Since different compounds have different molecular structures and bond energies, the size of the activation energy determines the difficulty of the reaction. In the separation process of normal alkanes, special attention should be paid to the differences in these activation energies because they directly affect the order and rate of formation of different products during the reaction. For example, reactions with higher activation energies usually require higher temperatures or longer reaction times to proceed smoothly, while reactions with lower activation energies can be converted at lower temperatures. Therefore, by adjusting the reaction conditions to optimize these activation energies, it can help to preferentially separate normal alkanes in a complex reaction network of multiple compounds. Another important part of this formula is This is a balance adjustment term used to describe whether the process of generating the i-th product is close to chemical equilibrium. As the product concentration gradually approaches its maximum equilibrium concentration C i，max , the generation rate will gradually decrease. This term ensures that when the concentration of the product approaches equilibrium, the reaction rate slows down to avoid the generation of too many by-products. In the separation process of normal alkanes, this term is very critical because it can effectively avoid the generation of excessive impurities and thus improve the separation purity. The role of pressure P in the formula is mainly reflected in the thermodynamic term, which affects the generation and consumption rate of the product by adjusting the pressure of the system. Specifically, the pressure term in the equation Combined with enthalpy change and entropy change These two reflect the thermodynamic properties of the system. The enthalpy change reflects the amount of heat absorbed or released during the reaction, while the entropy change describes the change in disorder of the system. By adjusting the reaction pressure, these thermodynamic factors can be controlled to make the formation of normal alkanes more favorable. For example, at higher pressures, reactions with larger enthalpy changes will be more likely to occur, while reactions with larger entropy changes may be more favorable at lower pressures.

[0057] Example 3: The optimization model is a genetic algorithm model; when the genetic algorithm model optimizes the reaction temperature T and the reaction pressure P of the separation tower, its fitness function F GA (T, P) is expressed using the following formula:

[0058]

[0059] Where m is the number of products; n is the number of normal alkane products; C k (T, P) is the concentration of the kth normal alkane product at reaction temperature T and reaction pressure P, C total (T, P) is the total concentration of all products, λ is a balance weight factor; C l (T, P) is the concentration of the first non-normal alkane product at reaction temperature T and reaction pressure P; through iterative optimization of the genetic algorithm model, a set of optimal reaction temperatures T is finally found. opt and the optimal reaction pressure Popt , so that the separation efficiency of normal alkanes reaches the maximum value:

[0060] (T opt , P opt )=argmaxF GA (T, P);

[0061] The optimal reaction temperature T opt As the optimal reaction temperature and the optimal reaction pressure P opt As the optimized reaction pressure.

[0062] Specifically, the genetic algorithm is an optimization algorithm based on natural selection and genetic mechanism, which gradually optimizes the performance of the system by simulating the biological evolution process. In this separation process, the core idea of ​​the genetic algorithm is to define the fitness function F GA (T, P) is used to evaluate the separation effect of normal alkanes under different reaction conditions, and then the best reaction temperature and pressure combination is found through iterative optimization. GA (T, P) is the key to optimization. Its main function is to quantify the relative concentration of n-alkanes and other byproducts at a specific temperature T and pressure P in the separation tower, thereby providing a basis for the selection, crossover and mutation of the genetic algorithm. Specifically, the fitness function is structured as two weighted sums: the first term represents the concentration proportion of n-alkanes, that is, the sum of the concentrations of all n-alkanes products C k (T, P) and total concentration C total The sum of the ratios of (T, P) represents the enrichment of normal alkanes under the reaction conditions; the second term represents the concentration ratio of non-normal alkane products, but this term is adjusted by a negative weight factor λ. The weight factor λ is used to balance the relative ratio between normal alkanes and non-normal alkanes, ensuring that the main goal during optimization is to improve the separation of normal alkanes while minimizing the generation of by-products.

[0063] The kth term in the equation is the concentration C of normal alkanes under different reaction conditions. k (T, P), which changes with temperature and pressure. By normalizing the concentrations of these products, that is, with the total product concentration C total The ratio of (T, P) can be used to determine the proportion of each normal alkane in the total product. The higher the ratio, the more normal alkanes are produced under the current reaction conditions, and the higher the separation efficiency. At the same time, the second part of the equation takes into account the concentration C of the non-normal alkane products. l(T, P), this part represents other impurities that are not desired to be generated during the reaction. By normalizing the concentrations of these impurities and multiplying them by a negative weight factor λ, reaction conditions that will increase the production of non-normal alkane products can be effectively penalized. This design allows the fitness function to tend to select reaction conditions that are conducive to the enrichment of normal alkanes and inhibit the production of by-products during the optimization process. As the genetic algorithm iterates, the fitness function will gradually improve the selection of reaction conditions. In each generation of iteration, the genetic algorithm evaluates a set of candidate solutions (i.e., a set of different T and P values) and selects solutions with higher fitness for "reproduction". These better solutions generate new candidate solutions through operations such as crossover and mutation, and their fitness is evaluated again in the next generation. After multiple iterations, the algorithm gradually converges to a set of optimal reaction temperature and pressure combinations (T opt , P opt ), so that the fitness function F GA (T, P) reaches the maximum value, that is, the separation efficiency of normal alkanes is maximized.

[0064] In this optimization process, the role of reaction temperature T and reaction pressure P is particularly important. Temperature controls the reaction rate and the overcoming of activation energy, while pressure affects the reaction equilibrium and intermolecular interactions. For example, higher temperatures may accelerate the cracking of alkanes in coal-based oil, thereby generating more normal alkanes, but if the temperature is too high, it may cause the normal alkanes to further crack into smaller molecules or gaseous products. Therefore, it is very critical to find a suitable temperature window. Similarly, the regulation of pressure is also crucial. Higher pressures usually make the reaction more conducive to the generation of liquid alkanes and inhibit the generation of gaseous by-products, thereby improving the separation efficiency of normal alkanes. Therefore, the goal of optimization is not only to adjust the temperature or pressure alone, but to consider the synergistic effect of the two at the same time, so that the system operates under the most favorable conditions. The advantage of genetic algorithms lies in their global search capabilities and strong adaptability to complex problems. Traditional optimization methods such as gradient descent may fall into local optimal solutions, while genetic algorithms can better explore the entire parameter space through the "evolution" process and avoid falling into local optimal solutions. In addition, genetic algorithms do not require continuity or differentiability of functions, which makes them very suitable for dealing with complex chemical reaction systems, especially processes such as coal-based oil separation involving multiple reaction products and complex dynamics. Finally, through the optimization of genetic algorithms, the optimal reaction temperature T is found. opt and the optimal reaction pressure P optIt can significantly improve the separation efficiency of normal alkanes and maximize the purity and yield of the products. Using this set of optimal parameters as the control conditions in actual production can not only improve the separation efficiency, but also reduce energy consumption and by-product generation, thereby improving the economy and environmental protection of the entire process. This method provides an advanced optimization method for the efficient separation of normal alkane phase change materials prepared from coal-based oil, and has broad industrial application prospects.

[0065] Example 4: Calculation of the adsorption rate θ of the kth normal alkane product in the molecular sieve by the Langmuir adsorption model k The formula is:

[0066]

[0067] in, The normal alkane products are at the optimal reaction temperature T opt The adsorption constant under .

[0068] Specifically, the Langmuir adsorption model assumes that there are a finite number of adsorption sites on the surface of the molecular sieve, which can adsorb normal alkane molecules. Each site can only adsorb one molecule, and the adsorption occurs on a monolayer surface, that is, multilayer adsorption does not occur. The adsorption equilibrium constant Reflects the optimal temperature T opt The interaction strength between normal alkanes and molecular sieves under the condition of . This constant is inversely proportional to the temperature. Increasing temperature usually reduces the adsorption constant K, because higher temperature makes the molecules gain more energy and are more inclined to desorb from the adsorption surface. The pressure P in the formula opt is the partial pressure of gaseous n-alkanes in the system. As the pressure increases, the adsorption rate of n-alkane molecules on the molecular sieve surface increases. This is because as the number of n-alkane molecules in the gas phase increases, more molecules have the opportunity to contact the surface of the molecular sieve and be adsorbed. At lower pressures, the sites on the molecular sieve surface may not be fully utilized, so the adsorption rate is low; when the pressure rises, more adsorption sites are occupied, and the adsorption rate gradually tends to saturation.

[0069] Adsorption rateθ k describes the proportion of the kth normal alkane product adsorbed on the molecular sieve, and its value is between 0 and 1. k = 0, indicating that no normal alkane molecules are adsorbed; when θ k = 1, indicating that all available adsorption sites are occupied by normal alkane molecules, reaching a fully saturated state. reflects the dynamics of adsorption equilibrium, as the pressure P opt Or adsorption constant The increase of adsorption rate θ kwill gradually approach 1, but will never exceed 1, which is the physical limit of the Langmuir model. The use of this model is very important in the context of the present invention because it provides a quantitative way to describe the separation efficiency of normal alkanes on molecular sieves. By optimizing temperature and pressure, the adsorption behavior of normal alkane molecules on the molecular sieve surface can be precisely controlled, thereby improving the separation efficiency. Especially in industrial applications, understanding the adsorption rate θ k The changing rules of the adsorption rate of n-alkanes can help better design and control the reaction conditions. For example, at higher pressures and suitable temperatures, the adsorption rate of n-alkanes will be higher, thereby improving the utilization efficiency of molecular sieves and reducing energy consumption and material waste in the separation process. In addition, the adsorption behavior of n-alkanes and other by-products on molecular sieves may be different. By comparing the adsorption rates of n-alkanes and non-n-alkanes on molecular sieves, the separation process can be further optimized, the conditions that are most conducive to the adsorption of n-alkanes can be selected, and the interference of impurities can be reduced, thereby improving the purity and efficiency of the separation. Therefore, through the calculation of the Langmuir adsorption model, theoretical support can be provided for the entire separation process to ensure that the separation efficiency of n-alkanes is maximized in actual operations.

[0070] Example 5: The objective function of the particle swarm optimization algorithm model in step 3 is defined as:

[0071]

[0072] in, is the extraction temperature in the optimized supercritical fluid extraction process; is the extraction pressure in the optimized supercritical fluid extraction process; S opt Solvent selection for optimized supercritical fluid extraction process.

[0073] Specifically, in Example 5 of the present invention, a particle swarm optimization algorithm (PSO) is used to optimize three key parameters in the supercritical fluid extraction process: extraction temperature Extraction pressure and solvent selection S opt Through the particle swarm optimization algorithm, the goal is to maximize the objective function E PSO (T S , P S , S) to ensure the best separation efficiency of normal alkanes in the supercritical fluid extraction process.

[0074] First of all, supercritical fluid extraction technology is an effective separation method, especially suitable for the separation of complex mixtures. It uses fluids in a supercritical state (i.e., substances that are neither gaseous nor liquid under high temperature and high pressure) to significantly improve separation efficiency. For the separation of normal alkanes, the key lies in how to choose the appropriate extraction temperature T S , extraction pressure PS and solvent S, thereby achieving efficient separation and purification under supercritical conditions. In order to optimize this process, the present invention adopts a particle swarm optimization algorithm to find the optimal solution that maximizes the separation efficiency by iteratively updating the combination of temperature, pressure and solvent. The working principle of the particle swarm optimization algorithm is to simulate the group behavior of a flock of birds or a school of fish. Each "particle" represents a possible parameter combination, namely T S , P S and a specific value of S. Each particle continuously updates its position and velocity based on its current state and the performance of other particles in the group, gradually approaching the global optimal solution. In each round of iteration, the objective function E PSO (T S , P S , S) is used to evaluate the “fitness” of each particle, that is, whether this set of parameters can maximize the extraction efficiency of n-alkanes under the current conditions.

[0075] The extraction performance of supercritical fluid is highly sensitive to temperature. Higher temperatures generally improve the diffusion capacity of the fluid, allowing it to better dissolve and separate the target substance n-alkanes. However, too high a temperature may reduce the solubility of the fluid, thereby affecting the extraction efficiency. Therefore, finding a suitable temperature range is crucial to ensure efficient separation. In particle swarm optimization, each particle corresponds to a specific temperature value, and the temperature is gradually adjusted through iteration to ensure that n-alkanes can be optimally extracted under the current conditions. Another key parameter in supercritical fluid extraction is pressure. Pressure determines the density of the fluid, which affects its solubility. Higher pressure can increase the density of the fluid, making it more solubilizing, which helps to separate n-alkanes. However, too high a pressure may increase the operating cost of the system and may affect the extraction selectivity in some cases. Therefore, in particle swarm optimization, by dynamically adjusting the pressure value of each particle, an optimal pressure can be found so that n-alkanes can be extracted under the most economical and efficient conditions. The choice of solvent is one of the core issues in supercritical fluid extraction. Different solvents have different physical and chemical properties in the supercritical state, including polarity, viscosity, density and solubility, which directly affect the separation efficiency of n-alkanes. Particle swarm optimization finds the most suitable solvent for separating n-alkanes by constantly trying different solvent combinations. For example, carbon dioxide is a common supercritical fluid solvent, but it may require the addition of a co-solvent to increase its solubility in n-alkanes. Through the iterative update of the particle swarm algorithm, the choice of solvent will gradually tend to the optimal combination.

[0076] The iterative process of the particle swarm optimization algorithm can dynamically explore the combination of these parameters, and has global search capabilities, and is not easily trapped in the local optimum. Each generation of particle swarms will evaluate the pros and cons of the current solution, and conduct a global search through "group wisdom", constantly adjusting the selection of temperature, pressure and solvent, and finally find the optimal parameter combination. This optimization process not only greatly improved the extraction efficiency of n-alkanes, but also reduced energy consumption and production costs, ensuring the economy of the entire separation process. It is worth noting that the advantage of particle swarm optimization lies in the simplicity and rapid convergence of its algorithm, which is particularly suitable for complex problems with multiple dimensions and parameters. Compared with traditional experimental methods or simple optimization methods, particle swarm optimization can simultaneously consider the mutual influence of multiple factors by simulating group collaborative behavior and quickly find the global optimal solution. In addition, the algorithm does not need to rely on the gradient information of the objective function, and is particularly suitable for dealing with nonlinear and multi-extreme problems, which gives it a significant advantage in the separation optimization of n-alkanes.

[0077] Example 6: E PSO (T s , P s , S) is the optimization function of the particle swarm optimization algorithm model, which is defined as:

[0078]

[0079] Among them, S k (T s , P s ) is in T s and P s The solubility of solvent S for the kth normal alkane product under the conditions; θ total is the total adsorption rate of all n-alkane products; C l (T s , P s ) is the first non-normal alkane product in T and P s The concentration under the condition; C feed is the total concentration of all products in the feed entering the extraction process; n is the total number of normal alkane products.

[0080] Specifically, the first term of the formula involves the adsorption rate θ of the n-alkane product k under different conditions k and solubility S k (T S , P S ). Adsorption rate θ k represents the adsorption ratio of the kth normal alkane product in the molecular sieve, indicating its separation tendency under the current conditions. A higher separation purity can be achieved through the selective adsorption of normal alkanes by the molecular sieve. On the other hand, the solubility S k (T S , PS ) means that at the extraction temperature T S and extraction pressure P S The solubility of the solvent for n-alkanes under the condition of θ. The higher the solubility, the more effectively the solvent can dissolve and separate n-alkanes. The combination of the two reflects the comprehensive effect of n-alkanes being extracted by the solvent under specific conditions and entering the molecular sieve adsorption process. Through this formula, the performance of n-alkanes in the dissolution and adsorption process is weighted and summed, and finally an overall dissolution and adsorption efficiency is obtained. The adsorption rate of n-alkanes θ k is normalized to the total adsorption rate θ total to ensure that under different conditions, the weighted sum can reasonably reflect the relative contribution of each n-alkane product.

[0081] Item 2 is a penalty term used to suppress the formation of non-normal alkane by-products. Here, λ is a balance weight factor used to control the by-product concentration C L (T S , P S ) on the optimization results. The generation of by-products may affect the separation efficiency of normal alkanes, increase the complexity of the system, and may bring unnecessary costs. Therefore, by setting the penalty term, the optimization function tends to select those parameter combinations that can minimize the generation of by-products. Specifically, the by-product concentration C L (T S , P S ) and feed concentration C feed The ratio of represents the concentration ratio of by-products to total products. As the concentration of by-products increases, the value of the penalty term increases, thereby reducing the overall fitness function value. This design ensures that during the particle swarm optimization process, the model will favor reaction conditions that can improve the separation efficiency of normal alkanes while reducing the generation of by-products, thereby improving the selectivity of the entire extraction process. Through this design, E PSO (T S , P S The objective function combines the solubility, adsorption rate and inhibition of by-products of normal alkanes to achieve comprehensive optimization of multiple parameters. The particle swarm optimization algorithm will iteratively calculate the value of this function, gradually adjust different parameter combinations, and find the optimal solution that can maximize the objective function, that is, the optimal extraction temperature. Optimal extraction pressure And the optimal solvent S optThis process can effectively improve the extraction efficiency of normal alkanes while inhibiting the generation of by-products, thereby improving the separation effect of the entire process. The particle swarm optimization algorithm finds the global optimal solution by simulating group intelligence. In each iteration, the algorithm generates multiple "particles", that is, different combinations of temperature, pressure and solvent. The fitness of each particle is determined by E PSO (T S , P S , S) function determines that particles with higher fitness will be considered as better solutions. By updating the position and velocity of particles, the particle swarm will gradually converge and eventually find the global optimal parameter combination. In actual operation, the optimized parameter combination can significantly improve the separation purity and yield of normal alkanes and reduce the generation of by-products. This not only improves production efficiency, but also reduces the complexity of subsequent processing and purification, thereby improving the economy and sustainability of the process.

[0082] Example 7: The method also includes: Step 4: Based on the optimized extraction temperature, solvent selection and extraction pressure in the supercritical fluid extraction process, the Peng-Robinson equation is used to describe the gas-liquid equilibrium of the normal alkane to calculate the compression factor, and the phase separation efficiency of the normal alkane is calculated through the compression factor; based on the phase separation efficiency of the normal alkane, the energy storage efficiency of the normal alkane as a phase change material is calculated.

[0083] Specifically, the Peng-Robinson equation, as a state equation widely used for non-ideal gases, can describe the gas-liquid phase behavior of substances at different temperatures and pressures. Since the separation efficiency of n-alkanes under supercritical extraction conditions is highly dependent on their equilibrium state in the gas-liquid two-phase, it is very appropriate to use the Peng-Robinson equation to describe the gas-liquid equilibrium. This equation takes into account the volume of n-alkane molecules and the interaction between molecules, which have a significant effect on the gas-liquid equilibrium under non-ideal conditions. Through the Peng-Robinson equation, it is possible to and pressure The behavior of n-alkanes in supercritical solvents is accurately described and their distribution state in the two phases is determined. This process is crucial because the phase behavior of n-alkanes determines their adsorption efficiency in molecular sieves and dissolution efficiency in solvents, which directly affect their separation efficiency. Based on the gas-liquid equilibrium state determined by the Peng-Robinson equation, the compressibility factor of n-alkanes is calculated next. The compressibility factor Z is an important parameter that describes the deviation of gas from the ideal gas behavior. For n-alkanes, the compressibility factor provides insight into their molecular state, especially under supercritical conditions of high temperature and pressure. By calculating the compressibility factor, we can further understand the phase behavior of n-alkanes under reaction conditions, that is, how its ratio in the gas phase and liquid phase changes. When the compressibility factor is close to 1, n-alkanes behave like ideal gases; when the compressibility factor deviates from 1, it means that n-alkanes undergo significant gas-liquid interconversion, which is crucial for phase separation efficiency. The size of the compressibility factor directly determines the compressibility and phase behavior of n-alkanes at different pressures and temperatures, which provides key data for the subsequent calculation of phase separation efficiency.

[0084] The phase separation efficiency of n-alkanes refers to the efficiency with which n-alkanes can be successfully separated from coal-based oil under optimized supercritical fluid extraction conditions. The calculation of compression factor and gas-liquid equilibrium can accurately evaluate how n-alkanes are extracted from complex coal-based oil mixtures under current conditions. This process relies on the physical properties of supercritical fluid extraction, especially the solubility and selectivity of the solvent for n-alkanes in the supercritical state. The optimization of gas-liquid equilibrium ensures the enrichment of n-alkanes in the liquid phase and the effective extraction from the gas phase by molecular sieves or other separation techniques. The efficient separation of n-alkanes is crucial for their application as phase change materials, because the energy storage efficiency is highly dependent on their purity and the stability of their phase change behavior. Finally, the phase separation efficiency of n-alkanes directly affects their energy storage efficiency as phase change materials. The core principle of phase change materials is to store or release energy by absorbing or releasing a large amount of latent heat during the phase change process. Therefore, the phase change behavior of n-alkanes between gas and liquid phases determines whether it can be used for energy storage efficiently. By optimizing the extraction temperature, pressure and solvent selection, the separation efficiency of n-alkanes is improved, and the phase change behavior in the phase change material is more stable and controllable. When calculating the energy storage efficiency of n-alkanes, based on the phase separation efficiency and the energy absorbed or released during the phase change process, its energy storage potential in practical applications can be evaluated. This is not only of great significance for n-alkanes as energy storage materials, but also can further expand their applications in the fields of energy management and thermal management.

[0085] Example 8: The compression factor is calculated by the following formula:

[0086]

[0087] Where Z is the compression factor;

[0088] The phase separation efficiency of normal alkanes is calculated by the compression factor using the following formula:

[0089]

[0090] Among them, P range is the pressure range; E k is the phase separation efficiency of the kth n-alkane product; is the vapor-liquid equilibrium concentration of the kth normal alkane product under the compression factor Z; is the concentration of the kth normal alkane product in the feed; A is the pre-crystallization factor; ΔG * is the critical free energy barrier; C is the supersaturation; S c is the critical supersaturation; B is the Boltzmann constant; T inf is the absolute temperature.

[0091] Specifically, in non-ideal gases, the behavior of n-alkanes is different from that of ideal gases, especially in the supercritical state of high pressure and high temperature, where the compressibility of the gas changes significantly. Therefore, the compression factor Z is needed to describe the degree to which its state deviates from the ideal gas. The Z in the formula is composed of multiple terms, which respectively reflect the supercritical temperature of n-alkanes after optimization. and pressure The first term is the basic ideal gas correction term, which describes the change in volume of a gas with changes in temperature and pressure. The next correction term involves the enthalpy change of n-alkanes and entropy change These parameters describe the energy changes of substances under different thermodynamic states. In particular, the enthalpy change term reflects the energy required for n-alkanes to transform from one state to another. These physical parameters work together to describe whether n-alkanes are in the gas phase or liquid phase under the current conditions. By calculating the compression factor Z, the phase separation efficiency of n-alkanes can be further derived. Phase separation efficiency E k The efficiency of separating n-alkanes from a complex mixture of coal-based oil under optimized supercritical conditions is described. This formula takes into account multiple physical quantities, including gas-liquid equilibrium concentration, pressure difference, free energy barrier, and supersaturation. First, the gas-liquid equilibrium concentration of n-alkanes under different compression factors Z is It is an important parameter that describes the distribution of normal alkanes in the gas and liquid phases under given conditions. Comparison can intuitively reflect the enrichment of normal alkanes in the separation process. The higher the concentration of normal alkanes in gas-liquid equilibrium, the better the separation efficiency.

[0092] At the same time, the pressure correction term in the formula The change of separation efficiency under different pressure conditions was considered. In the supercritical fluid extraction process, pressure is a crucial factor, which determines the density and solubility of the fluid. If the extraction pressure With the optimal pressure P opt If the pressure is close to 1, this term approaches 1, indicating that the separation efficiency reaches the maximum value; when the pressure deviates from the optimal condition, the separation efficiency decreases. This design ensures that the system always operates within the optimal pressure range to achieve the highest separation efficiency of normal alkanes. It reflects the kinetic factors of crystallization during phase transition. In the process of normal alkanes changing from liquid phase to solid phase, they need to overcome the free energy barrier ΔG. * , this energy barrier determines the rate of phase transition. By introducing the Boltzmann constant B and the absolute temperature T inf , which describes the thermodynamic behavior of the phase change process. In particular, the higher the temperature, the faster the system can overcome the free energy barrier, thereby accelerating the phase separation of n-alkanes. This section takes into account the key properties of n-alkanes as phase change materials, namely their phase change speed and energy storage capacity. In addition, the supersaturation C and critical supersaturation S in the formula c The ratio between It is an important factor to describe the stability of the phase separation process. Supersaturation C is a measure of the degree to which the solubility in the system exceeds the equilibrium state. When the supersaturation is large, the phase separation rate of normal alkanes is accelerated, which helps to enrich them. However, if the supersaturation is too high, it may lead to system instability and increased by-product formation. Critical supersaturation S c It is a threshold value that describes the maximum supersaturation that the system can withstand while maintaining stability. Therefore, by properly controlling the supersaturation, the separation process can maintain system stability while being efficient and avoiding the generation of excessive by-products.

[0093] Example 9: The energy storage efficiency of normal alkanes as phase change materials is calculated by the following formula:

[0094]

[0095] Among them, m PCM is the mass of the phase change material; ΔH f is the latent heat of phase change; T min is the minimum allowable temperature; T max is the maximum allowable temperature; C p (T X ) is the phase transition temperature T X Specific heat capacity of phase change material under .

[0096] Specifically, in Example 9 of the present invention, the energy storage efficiency of normal alkanes as phase change materials is calculated by a comprehensive formula that combines multiple factors such as the mass of the phase change material, the latent heat of phase change, the specific heat capacity, and the separation efficiency. The calculation formula is intended to quantify the overall energy storage capacity of normal alkanes in energy storage applications, especially the heat that can be stored during the phase change process.

[0097] First, the first part PCM ΔH f It represents the latent heat absorbed or released by the phase change material during the phase change process. Here, m PCM is the mass of the phase change material, that is, the total amount of normal alkanes in the system. Since the core function of the phase change material is to use the latent heat absorbed or released during the phase change process to achieve energy storage and release, the size of this term is directly related to the phase change latent heat of normal alkanes ΔH f The latent heat of phase change represents the amount of heat released or absorbed when a normal alkane changes from liquid to solid or from solid to liquid, expressed in energy per unit mass (e.g. J / kg). Therefore, the storage of this part of heat is one of the key characteristics of phase change materials and directly affects their energy storage performance. The higher the latent heat, the greater the energy stored in the material during the phase change process, thereby improving its energy storage efficiency. Part II represents the sensible heat stored by the phase change material through its specific heat capacity within the temperature range. This part of the calculation is based on the separation efficiency E of normal alkanes k , that is, in the aforementioned phase separation process, the better the purity and separation effect of normal alkanes, the higher the heat storage efficiency during the phase change process. Separation efficiency E k It is calculated by the aforementioned compression factor and phase separation efficiency, reflecting the performance of normal alkanes in the separation process. k The storage of this part of heat is corrected to a value directly related to the purity of normal alkanes, thus ensuring that only those normal alkanes that have been efficiently separated can participate in the efficient energy storage process. The normal alkanes in the temperature range T min to T max Specific heat capacity effect. Specific heat capacity C p (T) is a function of temperature and describes the material's ability to absorb or release heat at different temperatures. For phase change materials such as n-alkanes, the specific heat capacity changes significantly near the phase change temperature, so an accurate description of the material's specific heat capacity is critical to evaluating its energy storage performance. By integrating the specific heat capacity over this temperature range, the total amount of heat absorbed by n-alkanes when they undergo temperature changes can be accurately calculated. This portion of heat is the sensible heat storage of the material outside of the phase change.

[0098] For phase change materials, the latent heat storage usually dominates, especially when the material stores a large amount of energy through phase change. However, the heat storage of the specific heat capacity, although smaller, is also non-negligible over the entire temperature range. Especially in energy storage systems with large temperature changes, the sensible heat storage of the specific heat capacity can significantly improve the overall energy storage efficiency of the material. In addition, parameters such as the minimum allowable temperature T min and the maximum allowable temperature T max The temperature range in which the material operates in the energy storage system is set. These temperature boundary conditions are set according to system requirements to ensure that the application of n-alkanes in phase change materials can achieve optimal performance within a stable temperature range. For example, in some energy storage systems, n-alkanes may need to work within a specific temperature window. Therefore, by setting the upper and lower temperature limits, the heat storage and release behavior during its energy storage process can be effectively controlled to prevent performance degradation caused by excessively high or low temperatures. In general, this formula provides a complete calculation framework for the energy storage efficiency of n-alkanes as phase change materials. By combining the latent heat of phase change and specific heat capacity, the performance of n-alkanes in the energy storage process can be comprehensively evaluated. This optimization model not only takes into account the thermophysical properties of the material itself, but also combines the separation efficiency obtained in the aforementioned separation process to ensure that the material can achieve maximum energy storage efficiency under conditions of high purity. Ultimately, this calculation method provides theoretical support for the application of n-alkanes in the field of energy storage and demonstrates its great potential in industrial energy storage and thermal management.

[0099] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An efficient separation method for preparing normal alkane phase change materials from coal-based oil, characterized in that: The method comprises: Step 1: pre-treat the coal-based oil, set the reaction temperature and reaction pressure of the separation tower, decompose the components of the coal-based oil by multi-stage pyrolysis reaction, obtain various products, and calculate the concentration of various products; Step 2: Input the concentration of each product into the optimization model; the optimization model optimizes the reaction temperature and reaction pressure of the separation tower to maximize the separation efficiency of normal alkanes, and obtains the optimized reaction temperature and the optimized reaction pressure; Step 3: Based on the optimized reaction temperature and the optimized reaction pressure, the adsorption rate of n-alkanes in the molecular sieve is calculated by the Langmuir adsorption model; the adsorption rate is input into the particle swarm optimization algorithm model to optimize the solvent selection, extraction temperature and extraction pressure in the supercritical fluid extraction process to maximize the extraction efficiency.

2. The efficient separation method for preparing normal alkane phase change material from coal-based oil according to claim 1, characterized in that: The concentrations of various products were calculated by solving the following equations: Among them, C i is the concentration of the i-th product at time t; is the concentration of the jth product at the initial moment, that is, the input concentration at the beginning of the reaction; k i，j is the reaction rate constant for the conversion of the jth product to the ith product; E i，j is the activation energy of the conversion of the jth product into the ith product; T is the reaction temperature; R is the gas constant; C i，max is the maximum concentration of the ith product at equilibrium; t is the reaction time; is the standard enthalpy change when the concentration of the ith product changes; is the standard entropy change when the concentration of the ith product changes; P is the reaction pressure.

3. The efficient separation method for preparing normal alkane phase change material from coal-based oil as claimed in claim 2, characterized in that: The optimization model is a genetic algorithm model; when the genetic algorithm model optimizes the reaction temperature T and the reaction pressure P of the separation tower, its fitness function F GA (T, P) is expressed using the following formula: Where m is the number of products; n is the number of normal alkane products; C k (T, P) is the concentration of the kth normal alkane product at reaction temperature T and reaction pressure P, C total (T, P) is the total concentration of all products, λ is a balance weight factor; C l (T, P) is the concentration of the first non-normal alkane product at reaction temperature T and reaction pressure P; through iterative optimization of the genetic algorithm model, a set of optimal reaction temperatures T is finally found. opt and the optimal reaction pressure P opt , so that the separation efficiency of normal alkanes reaches the maximum value: (T opt ,P opt )=argmaxF GA (T,P); The optimal reaction temperature T opt As the optimal reaction temperature and the optimal reaction pressure P opt As the optimized reaction pressure.

4. The efficient separation method for preparing normal alkane phase change material from coal-based oil as claimed in claim 3, characterized in that: The adsorption rate θ of the kth normal alkane product in the molecular sieve is calculated by the Langmuir adsorption model. k The formula is: in, The normal alkane products are at the optimal reaction temperature T opt The adsorption constant under .

5. The efficient separation method for preparing normal alkane phase change material from coal-based oil as claimed in claim 4, characterized in that: The objective function of the particle swarm optimization algorithm model in step 3 is defined as: in, is the extraction temperature in the optimized supercritical fluid extraction process; is the extraction pressure in the optimized supercritical fluid extraction process; S opt Solvent selection for optimized supercritical fluid extraction process.

6. The efficient separation method for preparing normal alkane phase change material from coal-based oil as claimed in claim 5, characterized in that: E PSO (T s , P s , S) is the optimization function of the particle swarm optimization algorithm model, which is defined as: Among them, S k (T s , P s ) is in T s and P s The solubility of solvent S for the kth normal alkane product under the conditions; θ total is the total adsorption rate of all n-alkane products; C l (T s , P s ) is the first non-normal alkane product in T s and P s The concentration under the condition; C feed is the total concentration of all products in the feed entering the extraction process; n is the total number of normal alkane products.

7. The efficient separation method for preparing normal alkane phase change material from coal-based oil as claimed in claim 6, characterized in that: The method also includes: step 4: based on the optimized extraction temperature, solvent selection and extraction pressure in the supercritical fluid extraction process, using the Peng-Robinson equation to describe the gas-liquid equilibrium of the normal alkane to calculate the compression factor, and calculating the phase separation efficiency of the normal alkane through the compression factor; based on the phase separation efficiency of the normal alkane, calculating the energy storage efficiency of the normal alkane as a phase change material.

8. The efficient separation method for preparing normal alkane phase change material from coal-based oil as claimed in claim 7, characterized in that: The compression factor is calculated using the following formula: Where Z is the compression factor; The phase separation efficiency of normal alkanes is calculated by the compression factor using the following formula: Among them, P range is the pressure range; E k is the phase separation efficiency of the kth n-alkane product; is the vapor-liquid equilibrium concentration of the kth normal alkane product under the compression factor Z; is the concentration of the kth normal alkane product in the feed; A is the pre-crystallization factor; ΔG * is the critical free energy barrier; C is the supersaturation; S c is the critical supersaturation; B is the Boltzmann constant; T inf is the absolute temperature.

9. The efficient separation method for preparing normal alkane phase change material from coal-based oil as claimed in claim 8, characterized in that: The energy storage efficiency of normal alkanes as phase change materials is calculated by the following formula: Among them, m PCM is the mass of the phase change material; ΔH f is the latent heat of phase change; T min is the minimum allowable temperature; T max is the maximum allowable temperature; C p (T X ) is the phase transition temperature T X Specific heat capacity of phase change material under .

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