System and method for producing ethylene glycol by using raw gas

Abstract

The invention provides a system and method for producing ethylene glycol by using raw coke oven gas, and relates to the field of ethylene glycol production.The method comprises the steps that firstly, time queue data of adsorption pressure values and gas flow values are collected, and time sequence correlation of the adsorption pressure values and the gas flow values is conducted through a data analysis and processing technology based on deep learning; therefore, whether the adsorption pressure value at the next time point needs to be increased or not is automatically judged according to significant interaction representation of principal components between the adsorption pressure value time sequence correlation characteristics and the gas flow value time sequence correlation characteristics. Therefore, the adsorption pressure time sequence data and the gas flow time sequence data can be collected and analyzed in real time, so that the time sequence interaction dynamic change and fluctuation condition between the adsorption pressure and the gas flow in the adsorption process can be better understood, corresponding dynamic adjustment is realized, and the automation level of pressure swing adsorption process control is improved.

Description

Technical Field

[0001] The present application relates to the field of ethylene glycol production, and more specifically, to a system and method for producing ethylene glycol using raw coal gas. Background Art

[0002] Ethylene glycol, with the chemical formula HO-CH2-CH2-OH, is a colorless, odorless, sweet-tasting viscous liquid. It is non-volatile at room temperature and hygroscopic. Due to its unique chemical and physical properties, ethylene glycol is widely used in many industries such as automobiles, textiles, plastics, coatings, printing, and cosmetics.

[0003] However, the traditional method of producing ethylene glycol mainly relies on petroleum resources. Specifically, due to the reliance on petroleum as a raw material, the production cost is greatly affected by oil price fluctuations, and petroleum is a non-renewable resource, which poses a risk of resource depletion in the long run. In addition, in the multi-step conversion process from petroleum to ethylene glycol, each step requires a lot of energy, resulting in high overall energy consumption, and a large amount of greenhouse gases and other pollutants are generated during the entire production process, which puts pressure on the environment.

[0004] Therefore, an optimized scheme for producing ethylene glycol is desired. Summary of the invention

[0005] In order to solve the above technical problems, the present application is proposed. The embodiments of the present application provide a system and method for producing ethylene glycol using raw coal gas.

[0006] According to one aspect of the present application, there is provided a method for producing ethylene glycol using raw coal gas, comprising: S1: compressing the raw raw gas to obtain compressed raw gas; S2: converting the compressed raw coal gas to obtain converted gas; S3: performing shift treatment, acid removal and pressure swing adsorption on a portion of the gas after the conversion treatment to obtain hydrogen; S4: performing heat recovery, acid removal and pressure swing adsorption on another part of the gas after the conversion treatment to obtain carbon monoxide; S5: After the carbon monoxide is pressurized, the pressurized carbon monoxide and the hydrogen are introduced into an ethylene glycol device to obtain the ethylene glycol.

[0007] According to another aspect of the present application, a system for producing ethylene glycol using raw coal gas is provided, comprising: A compression processing module, used for compressing the raw raw coal gas introduced to obtain compressed raw coal gas; A conversion processing module, used for performing conversion processing on the compressed raw coal gas to obtain converted gas; A hydrogen extraction module, used for performing conversion treatment, acid removal purification and pressure swing adsorption on a portion of the gas after the conversion treatment to obtain hydrogen; A carbon monoxide extraction module, for performing heat recovery, acid removal and pressure swing adsorption on another part of the gas after the conversion treatment to obtain carbon monoxide; The ethylene glycol synthesis module is used to pressurize the carbon monoxide and then pass the pressurized carbon monoxide and the hydrogen into an ethylene glycol device to obtain the ethylene glycol.

[0008] Compared with the prior art, the system and method for producing ethylene glycol using raw coal gas provided by the present application first collects the time queue data of the adsorption pressure value and the gas flow value, and uses the data analysis and processing technology based on deep learning to perform the time series correlation of the adsorption pressure value and the gas flow value, so as to automatically judge whether the adsorption pressure value at the next time point needs to be increased according to the principal component significant interaction representation between the adsorption pressure value time series correlation feature and the gas flow value time series correlation feature. In this way, the adsorption pressure time series data and the gas flow time series data can be collected and analyzed in real time to better understand the dynamic changes and fluctuations of the time series interaction between the adsorption pressure and the gas flow in the adsorption process, so as to achieve corresponding dynamic adjustments, thereby improving the automation level of the pressure swing adsorption process control. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0010] Figure 1 The present invention is a flow chart of a method for producing ethylene glycol using raw coal gas according to an embodiment of the present application.

[0011] Figure 2 The present invention is a flow chart of a method for producing ethylene glycol by utilizing raw coal gas according to an embodiment of the present application, in which the compressed raw coal gas is subjected to conversion treatment to obtain converted gas.

[0012] Figure 3 The present invention is a flow chart of a method for producing ethylene glycol using raw coal gas according to an embodiment of the present application, in which a portion of the gas after the conversion treatment is subjected to shift treatment, acid removal purification and pressure swing adsorption to obtain hydrogen.

[0013] Figure 4The present invention is a flow chart of a method for producing ethylene glycol using raw coal gas according to an embodiment of the present application, in which the gas after acid removal and purification is subjected to pressure swing adsorption to obtain the hydrogen.

[0014] Figure 5 The present invention is a data flow diagram of the method for producing ethylene glycol using raw coal gas according to an embodiment of the present application, in which the acid-depurated gas is subjected to pressure swing adsorption to obtain the hydrogen.

[0015] Figure 6 In the method for producing ethylene glycol using raw coal gas according to an embodiment of the present application, the adsorption pressure time series correlation characteristic vector and the gas flow time series correlation characteristic vector are subjected to adsorption pressure-gas flow interactive response coupling to obtain a flow chart of significant interactive coupling characteristics of adsorption pressure-gas flow main component granularity.

[0016] Figure 7 The present invention is a flow chart of a method for producing ethylene glycol using raw coal gas according to an embodiment of the present application, in which another part of the gas after the conversion treatment is subjected to heat recovery, acid removal and pressure swing adsorption to obtain carbon monoxide.

[0017] Figure 8 It is a block diagram of a system for producing ethylene glycol using raw coal gas according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0019] Ethylene glycol, with the chemical formula HO-CH2-CH2-OH, is a colorless, odorless, sweet-tasting viscous liquid. It is non-volatile at room temperature and has hygroscopic properties. Due to its unique chemical and physical properties, ethylene glycol has been widely used in many fields such as automobiles, textiles, plastics, coatings, printing, and cosmetics.

[0020] However, the traditional production method of ethylene glycol mainly relies on petroleum resources. This dependence means that production costs are vulnerable to fluctuations in oil prices, and as a non-renewable resource, there is uncertainty in the long-term supply of oil. In addition, the conversion process from oil to ethylene glycol involves multiple steps, each of which is accompanied by a large amount of energy consumption, resulting in low overall energy efficiency. At the same time, this production chain also produces a large amount of greenhouse gases and other environmental pollutants, which imposes a significant burden on the environment.

[0021] It should be understood that raw coal gas contains convertible carbon monoxide (CO) and hydrogen (H2), which are important raw materials for synthesizing ethylene glycol. Through advanced catalytic technology and gas purification methods, CO and H2 in raw coal gas can be first converted into synthesis gas, and then finally generate ethylene glycol through a series of chemical reactions.

[0022] Based on this, the present application proposes a method for producing ethylene glycol using raw coal gas. Figure 1 Flow chart of the method for producing ethylene glycol using raw coal gas according to an embodiment of the present application. Figure 1 As shown, according to the embodiment of the present application, the method for producing ethylene glycol using raw coal gas includes: S1: compressing the raw raw coal gas to obtain compressed raw coal gas; S2: converting the compressed raw coal gas to obtain converted gas; S3: performing transformation treatment, acid depuration and pressure swing adsorption on a part of the converted gas to obtain hydrogen; S4: performing heat recovery, acid depuration and pressure swing adsorption on another part of the converted gas to obtain carbon monoxide; S5: after pressurizing the carbon monoxide, the pressurized carbon monoxide and the hydrogen are introduced into an ethylene glycol device to obtain the ethylene glycol.

[0023] In step S1, the raw raw gas is compressed to obtain compressed raw gas. It should be understood that in order to meet the subsequent acid removal and PSA (pressure swing adsorption) operation requirements of the raw raw gas, the raw raw gas needs to be compressed first. Before the compression process, the raw gas needs to be pretreated to remove saturated water and some impurities such as oil dust, and then the pretreated raw gas is passed into the compressor for specific compression processing operations.

[0024] In step S2, the compressed raw coal gas is subjected to a conversion treatment to obtain a converted gas. Specifically, Figure 2 The present invention is a flow chart of converting the compressed raw coal gas to obtain the converted gas in the method for producing ethylene glycol using raw coal gas according to an embodiment of the present application. Figure 2 As shown, the compressed raw coal gas is subjected to conversion treatment to obtain converted gas, including: S21: non-catalytic partial oxidation treatment of the compressed raw coal gas to obtain oxidized raw coal gas; S22: high-temperature conversion reaction of the oxidized raw coal gas to obtain the converted gas.

[0025] In step S21, the compressed raw coal gas is subjected to non-catalytic partial oxidation treatment to obtain oxidized raw coal gas. It should be understood that in order to convert methane and other hydrocarbon compounds in the raw coal gas into effective synthesis gas (specifically CO and H2) with high added value, which is used to subsequently generate chemical products such as ethylene glycol, the compressed raw coal gas needs to be subjected to non-catalytic partial oxidation treatment first.

[0026] In step S22, the raw coal gas after oxidation treatment is subjected to a high-temperature conversion reaction to obtain the converted gas. Accordingly, considering that there are some harmful substances in the raw coal gas, such as organic sulfur, sulfide, etc., in order to remove the harmful substances in the raw coal gas, it is necessary to perform a high-temperature conversion reaction on the raw coal gas after oxidation treatment. Through the high-temperature conversion reaction, the harmful substances can be decomposed or converted into non-toxic or easy-to-handle components, which can reduce environmental pollution and reduce the harm to the environment in the process of producing ethylene glycol.

[0027] In step S3, a portion of the gas after the conversion treatment is subjected to shift treatment, acid removal and pressure swing adsorption to obtain hydrogen. Specifically, Figure 3 The present invention is a flow chart of a method for producing ethylene glycol from raw coal gas according to an embodiment of the present application, wherein a portion of the gas after the conversion treatment is subjected to conversion treatment, acid removal and purification, and pressure swing adsorption to obtain hydrogen. Figure 3 As shown, a portion of the converted gas is subjected to transformation treatment, acid depuration and pressure swing adsorption to obtain hydrogen, including: S31: transforming a portion of the converted gas to obtain transformed gas; S32: performing low-temperature methanol washing on the converted gas to obtain acid depurated gas; S33: performing pressure swing adsorption on the acid depurated gas to obtain the hydrogen.

[0028] In step S31, a portion of the converted gas is subjected to a shift treatment to obtain a converted gas. In the present application, the shift treatment specifically refers to the conversion of CO and H2O in the converted gas to produce H2 and CO2 under certain pressure and temperature conditions under the action of a catalyst. By performing the shift treatment, the composition of the gas can be adjusted to meet the demand for H2 and CO in the ethylene glycol production process.

[0029] In step S32, the gas after the conversion treatment is subjected to low-temperature methanol washing treatment to obtain acid-depurated gas. Accordingly, considering that the solubility and absorption of methanol in a low-temperature state to different gases are different, it has a large solubility and absorption to some acidic gases, but a small solubility and absorption to other gases. In order to purify the raw gas and remove the acidic gas in the gas after the conversion treatment, the gas after the conversion treatment needs to be subjected to low-temperature methanol washing treatment. By removing the acidic gas, the purity of the gas can be significantly improved to meet the requirements of the subsequent process for the purity of the raw gas.

[0030] In step S33, the acid-purified gas is subjected to pressure swing adsorption to obtain the hydrogen.

[0031] It should be understood that although the gas after the conversion treatment has been purified to a certain extent, it still contains various impurity gases, such as carbon monoxide, carbon dioxide, nitrogen, etc. In order to meet specific needs in industrial applications, these impurities need to be further removed to obtain high-purity hydrogen. However, traditional pressure swing adsorption usually adopts a fixed operating procedure, that is, the adjustment of the adsorption pressure is usually carried out according to a preset schedule, rather than dynamically adjusted according to the real-time adsorption situation. In the actual process, since the flow rate of the feed gas may fluctuate with time and changes in external conditions, and this fixed operating procedure is difficult to cope with the timing changes and fluctuations of the flow rate of the feed gas, it is difficult for the system to flexibly reach the optimal operating state.

[0032] Accordingly, in the process of pressure swing adsorption of the acid-purified gas to obtain the hydrogen, the technical concept of the present application is to collect the time queue of the adsorption pressure value by the pressure sensor, and collect the time queue of the gas flow value by the flow sensor, and use the data analysis and processing technology based on deep learning to perform the time series association of the adsorption pressure value and the gas flow value, so as to automatically judge whether the adsorption pressure value at the next time point needs to be increased according to the principal component significant interaction between the time series association characteristics of the adsorption pressure value and the time series association characteristics of the gas flow value. In this way, the adsorption pressure time series data and the gas flow time series data can be collected and analyzed in real time to better understand the dynamic changes and fluctuations of the time series interaction between the adsorption pressure and the gas flow during the adsorption process, so as to achieve corresponding dynamic adjustments, thereby improving the automation level of the pressure swing adsorption process control.

[0033] Specifically, Figure 4 The present invention is a flow chart of a method for producing ethylene glycol using raw coal gas according to an embodiment of the present application, in which the gas after acid removal and purification is subjected to pressure swing adsorption to obtain the hydrogen. Figure 5 The data flow diagram of the method for producing ethylene glycol using raw coal gas according to an embodiment of the present application is to perform pressure swing adsorption on the acid-purified gas to obtain the hydrogen. Figure 4 and Figure 5 As shown, the gas after acid depuration is subjected to pressure swing adsorption to obtain the hydrogen, including: S331: obtaining a time queue of adsorption pressure values ​​collected by a pressure sensor; S332: obtaining a time queue of gas flow values ​​collected by a flow sensor; S333: performing sequence encoding on the time queue of adsorption pressure values ​​and the time queue of gas flow values ​​to obtain an adsorption pressure time series correlation feature vector and a gas flow time series correlation feature vector; S334: performing adsorption pressure-gas flow interactive response coupling on the adsorption pressure time series correlation feature vector and the gas flow time series correlation feature vector to obtain a significant interactive coupling feature of the adsorption pressure-gas flow main component granularity; S335: based on the significant interactive coupling feature of the adsorption pressure-gas flow main component granularity, obtaining an optimization instruction, the optimization instruction is used to indicate whether the adsorption pressure value at the next time point needs to be increased.

[0034] In step S331 and step S332, the time queue of the adsorption pressure value collected by the pressure sensor and the time queue of the gas flow value collected by the flow sensor are respectively obtained. It should be understood that in the pressure swing adsorption process, the adsorption pressure and the gas flow rate are key parameters. Specifically, the adsorption pressure is the pressure value inside the adsorption tower during the pressure swing adsorption process, and the gas flow rate value refers to the amount of gas flowing through the adsorption tower per unit time. Therefore, in order to ensure that the adsorbent works at the correct pressure, so as to better control the pressure swing adsorption process, in the technical solution of the present application, the time queue of the adsorption pressure value collected by the pressure sensor is obtained, and the time queue of the gas flow rate value collected by the flow sensor is obtained.

[0035] In step S333, the time queue of the adsorption pressure value and the time queue of the gas flow value are sequence-encoded to obtain the adsorption pressure time series correlation feature vector and the gas flow time series correlation feature vector. Specifically, in an embodiment of the present application, the time queue of the adsorption pressure value and the time queue of the gas flow value are sequence-encoded to obtain the adsorption pressure time series correlation feature vector and the gas flow time series correlation feature vector, including: inputting the time queue of the adsorption pressure value and the time queue of the gas flow value into a sequence encoder based on a forward LSTM model to obtain the adsorption pressure time series correlation feature vector and the gas flow time series correlation feature vector. It should be understood that the adsorption pressure value and the gas flow value have different degrees of time series correlation in different time periods, and the current pressure and flow may be related to the data of multiple previous time points. Considering that the forward LSTM model is good at processing sequence data with long-term dependencies, it can remember important historical information and capture the time series correlation between different time spans. Therefore, in the technical solution of the present application, the time queue of the adsorption pressure value and the time queue of the gas flow value are input into a sequence encoder based on a forward LSTM model to capture and mine the temporal associations between different time spans, and obtain the adsorption pressure temporal association feature vector and the gas flow temporal association feature vector.

[0036] In step S334, the adsorption pressure time series correlation feature vector and the gas flow time series correlation feature vector are subjected to adsorption pressure-gas flow interactive response coupling to obtain the adsorption pressure-gas flow main component particle size significant interactive coupling feature. Specifically, Figure 6 In the method for producing ethylene glycol using raw coal gas according to an embodiment of the present application, the adsorption pressure time series correlation feature vector and the gas flow time series correlation feature vector are subjected to adsorption pressure-gas flow interactive response coupling to obtain the adsorption pressure-gas flow main component granularity significant interactive coupling feature. Figure 6 As shown, the adsorption pressure time series correlation characteristic vector and the gas flow time series correlation characteristic vector are subjected to adsorption pressure-gas flow interactive response coupling to obtain the adsorption pressure-gas flow main component granularity significant interactive coupling characteristics, including: S3341: principal component analysis is performed on the adsorption pressure time series correlation characteristic vector and the gas flow time series correlation characteristic vector to obtain a set of adsorption pressure time series correlation main component characteristic components and a set of gas flow time series correlation main component characteristic components; S3342: optimal interactive matching coupling is performed on the set of adsorption pressure time series correlation main component characteristic components and the set of gas flow time series correlation main component characteristic components to obtain the adsorption pressure-gas flow main component granularity significant interactive coupling characteristics.

[0037] It should be understood that there is a mutual correlation relationship between the adsorption pressure time series correlation feature vector and the gas flow time series correlation feature vector in terms of time series, and both vectors contain key and main time series feature information. Therefore, in order to comprehensively analyze the complex interaction between the two feature vectors and extract the feature vector that can characterize the most significant interaction relationship between the two, in the technical solution of the present application, the adsorption pressure time series correlation feature vector and the gas flow time series correlation feature vector are coupled in an adsorption pressure-gas flow interactive response to obtain an adsorption pressure-gas flow main component granularity significant interactive coupling feature vector as an adsorption pressure-gas flow main component granularity significant interactive coupling feature.

[0038] Specifically, first, principal component analysis is performed on the adsorption pressure time series correlation feature vector and the gas flow time series correlation feature vector to obtain a set of adsorption pressure time series correlation principal component feature components and a set of gas flow time series correlation principal component feature components. In particular, principal component analysis (PCA) is a dimensionality reduction technique that can convert high-dimensional data into low-dimensional data while retaining as much variation information in the original data as possible. That is, through PCA, the original adsorption pressure time series correlation feature vector and the gas flow time series correlation feature vector can be converted into a set of new orthogonal feature components, namely, principal components, and the most important feature information in the original data is retained.

[0039] Next, the set of the adsorption pressure time series associated principal component characteristic components and the set of the gas flow time series associated principal component characteristic components are optimally interactively matched and coupled to obtain the adsorption pressure-gas flow principal component granularity significant interactive coupling characteristic vector as the adsorption pressure-gas flow principal component granularity significant interactive coupling feature. Specifically, taking each of the adsorption pressure time series associated principal component characteristic components as the query vector, and the set of the gas flow time series associated principal component characteristic components as the query library, each of the adsorption pressure time series associated principal component characteristic components is optimally matched to obtain the set of {adsorption pressure time series associated principal component characteristic components; gas flow time series associated principal component characteristic components} best matching pairs. That is, the best matching pair can identify the main interactive relationship between the adsorption pressure and the gas flow, and help select the most representative feature combination, which can better reflect the dynamic relationship between the adsorption pressure and the gas flow, so as to reveal which adsorption pressure principal components and gas flow principal components have the strongest correlation or influence. In particular, in the technical solution of the present application, the Mahalanobis distance between the two is calculated as the matching value between the two, and the pair with the smallest distance in the matching value is the best matching pair. Afterwards, in order to further capture and mine the deep semantic associations and multi-scale and multi-level interactive relationships between each group of best matching pairs, the multi-scale interactive response coupling module is used to process the best matching pairs to generate a rich set of multi-scale interactive coupling representation vectors of the best matching pairs of adsorption pressure and gas flow. Finally, each coupling representation vector is cascaded to comprehensively and comprehensively characterize the most relevant and significant feature information between adsorption pressure and gas flow, and obtain the adsorption pressure-gas flow main component granularity significant interactive coupling feature vector.

[0040] Specifically, in an embodiment of the present application, the set of the adsorption pressure time series associated principal component characteristic components and the set of the gas flow time series associated principal component characteristic components are optimally interactively matched and coupled to obtain the adsorption pressure-gas flow main component granularity significant interactive coupling characteristics, including: using each adsorption pressure time series associated principal component characteristic component in the set of the adsorption pressure time series associated principal component characteristic components as a query vector and using the set of the gas flow time series associated principal component characteristic components as a query library, matching the gas flow time series associated principal component characteristic components that best match each of the query vectors from the query library to obtain {adsorption pressure time series associated principal component characteristic components; gas flow ... a set of best matching pairs of {adsorption pressure time series associated principal component characteristic components; gas flow time series associated principal component characteristic components}; inputting each best matching pair of {adsorption pressure time series associated principal component characteristic components; gas flow time series associated principal component characteristic components} in the set of best matching pairs of {adsorption pressure time series associated principal component characteristic components; gas flow time series associated principal component characteristic components} into a multi-scale interactive response coupling module to obtain a set of multi-scale interactive coupling representation vectors of adsorption pressure-gas flow best matching pairs; cascading the set of multi-scale interactive coupling representation vectors of the adsorption pressure-gas flow best matching pairs to obtain the adsorption pressure-gas flow principal component granularity significant interactive coupling feature vector as the adsorption pressure-gas flow principal component granularity significant interactive coupling feature.

[0041] More specifically, in an embodiment of the present application, each adsorption pressure time series associated main component characteristic component in the set of the adsorption pressure time series associated main component characteristic components is used as a query vector and the set of the gas flow time series associated main component characteristic components is used as a query library, and the gas flow time series associated main component characteristic components that best match each of the query vectors are matched from the query library to obtain a set of best matching pairs of {adsorption pressure time series associated main component characteristic components; gas flow time series associated main component characteristic components}, including: extracting a predetermined adsorption pressure time series associated main component characteristic component from the set of the adsorption pressure time series associated main component characteristic components; calculating the Mahalanobis distance between the predetermined adsorption pressure time series associated main component characteristic component and each gas flow time series associated main component characteristic component in the set of the gas flow time series associated main component characteristic components to obtain a set of matching distances, wherein a pair of {adsorption pressure time series associated main component characteristic components; gas flow time series associated main component characteristic components} with the smallest matching distance in the set of matching distances is taken as the best matching pair.

[0042] More specifically, in an embodiment of the present application, each best matching pair of {adsorption pressure time series associated principal component characteristic component; gas flow time series associated principal component characteristic component} in the set of best matching pairs of {adsorption pressure time series associated principal component characteristic component; gas flow time series associated principal component characteristic component} is input into a multi-scale interactive response coupling module to obtain a set of multi-scale interactive coupling representation vectors of adsorption pressure-gas flow best matching pairs, including: respectively calculating the L2 norms of the adsorption pressure time series associated principal component characteristic component and the gas flow time series associated principal component characteristic component in the best matching pair of {adsorption pressure time series associated principal component characteristic component; gas flow time series associated principal component characteristic component} to obtain adsorption pressure norm value and gas flow norm value; extracting the maximum value from the adsorption pressure norm value, the gas flow norm value and the modulation parameter to obtain the interaction maximum value; respectively calculating the positional subtraction between the adsorption pressure time series associated principal component characteristic component and the gas flow time series associated principal component characteristic component, The adsorption pressure-gas flow time series associated principal component difference vector, the adsorption pressure-gas flow time series associated principal component point product vector and the adsorption pressure-gas flow time series associated principal component sum vector are obtained by performing position point multiplication and position addition; the adsorption pressure-gas flow time series associated principal component difference vector, the adsorption pressure-gas flow time series associated principal component point product vector and the adsorption pressure-gas flow time series associated principal component sum vector are cascaded to obtain the adsorption pressure-gas flow time series associated principal component cascade vector; the adsorption pressure-gas flow time series associated principal component cascade vector is one-dimensionally convolutionally encoded to obtain the adsorption pressure-gas flow time series associated principal component convolution encoding vector; the adsorption pressure-gas flow time series associated principal component convolution encoding vector is subjected to maximum pooling processing based on a local window, and the obtained adsorption pressure-gas flow time series interaction representation vector is divided by the interaction maximum value point by point to obtain the multi-scale interaction coupling representation vector of the adsorption pressure-gas flow best match.

[0043] In the embodiment of the present application, specifically, the adsorption pressure time series correlation characteristic vector and the gas flow time series correlation characteristic vector are subjected to adsorption pressure-gas flow interactive response coupling, which can be expressed by the formula: ;in, and are the adsorption pressure sample covariance matrix and the gas flow sample covariance matrix, and They are the principal component orthogonal matrix of adsorption pressure and the principal component orthogonal matrix of gas flow, is each adsorption pressure time series associated principal component characteristic component in the set of adsorption pressure time series associated principal component characteristic components, is each gas flow time series associated principal component characteristic component in the set of gas flow time series associated principal component characteristic components, and are the adsorption pressure diagonal matrix and the gas flow diagonal matrix respectively, are the first and second components of the set of principal component characteristic components associated with the adsorption pressure time series. The weight value of the characteristic component of the principal component associated with the adsorption pressure time series, are the first and second components of the gas flow time series associated principal component characteristic component set respectively. The weight value of the characteristic component of the main component associated with each gas flow time series, and They are and The transposed matrix of is the first characteristic component of the adsorption pressure time series associated principal component The principal component characteristic components of the adsorption pressure time series are for and The covariance matrix between To return the minimum value value, is the minimum approximate matching value, and They are position difference, position dot multiplication and position addition respectively. For cascade processing, is a one-dimensional convolutional coding operation, is the maximum pooling operation, To extract the maximum value, is the L2 norm of the vector, is the modulation parameter, and are the first, second, and third vectors in the set of multi-scale interaction coupling representation vectors of the best adsorption pressure-gas flow rate match. and The best matching vector of adsorption pressure-gas flow rate for multi-scale interaction coupling is represented by: is the adsorption pressure-gas flow rate principal component particle size significant interaction coupling eigenvector.

[0044] In step S335, based on the significant interactive coupling characteristics of the adsorption pressure-gas flow main component granularity, an optimization instruction is obtained, and the optimization instruction is used to indicate whether the adsorption pressure value at the next time point needs to be increased. Specifically, in an embodiment of the present application, based on the significant interactive coupling characteristics of the adsorption pressure-gas flow main component granularity, an optimization instruction is obtained, including: inputting the adsorption pressure-gas flow main component granularity significant interactive coupling feature vector into a control optimizer based on a classifier to obtain the optimization instruction. That is, the adsorption pressure-gas flow main component granularity significant interactive coupling characteristics obtained by significantly interactive coupling the adsorption pressure time series associated feature vector and the gas flow time series associated feature vector are used for classification processing to automatically determine whether the adsorption pressure value at the next time point needs to be increased. In this way, the adsorption pressure time series data and the gas flow time series data can be collected and analyzed in real time to better understand the dynamic changes and fluctuations of the time series interaction between the adsorption pressure and the gas flow in the adsorption process, so as to achieve corresponding dynamic adjustments, thereby improving the automation level of the pressure swing adsorption process control.

[0045] In particular, when the adsorption pressure time series correlation feature vector and the gas flow rate time series correlation feature vector respectively represent the bidirectional short-range-long-range time series correlation characteristics of the adsorption pressure value and the gas flow rate value, when performing interactive response coupling based on the optimal matching of the characteristic principal component granularity, the adsorption pressure-gas flow rate principal component granularity significant interactive coupling feature vector will also cause interactive response diversity based on the optimal matching due to the difference in the time series patterns of the characteristic principal component granularity, affecting the iterative consistency of the classification mapping and reducing the accuracy of the classification results.

[0046] Preferably, when the adsorption pressure-gas flow rate principal component particle size significant interactive coupling feature vector is input into a classifier-based control optimizer, the adsorption pressure-gas flow rate principal component particle size significant interactive coupling feature vector is first optimized, and the optimization process specifically includes: Determine the mean and variance of the feature set composed of the eigenvalues ​​of the adsorption pressure-gas flow rate main component particle size significant interactive coupling eigenvector, and divide the mean by the variance to obtain the adsorption pressure-gas flow rate main component particle size significant interactive coupling probability weight value: ;in, and represent the mean and variance of the feature set composed of the eigenvalues ​​of the adsorption pressure-gas flow rate principal component particle size significant interaction coupling eigenvector, It represents the probability weight value of significant interaction coupling between adsorption pressure and gas flow rate principal component particle size; The adsorption pressure-gas flow rate main component particle size significant interactive coupling eigenvector is multiplied by the inverse of the maximum eigenvalue of the adsorption pressure-gas flow rate main component particle size significant interactive coupling eigenvector to obtain the adsorption pressure-gas flow rate main component particle size significant interactive coupling probability constraint vector: ;in, represents the adsorption pressure-gas flow rate principal component particle size significant interaction coupling eigenvector, represents the maximum eigenvalue of the adsorption pressure-gas flow rate principal component particle size significant interaction coupling eigenvector, It means point multiplication by position. It represents the probability constraint vector of significant interaction coupling between adsorption pressure and gas flow rate principal component particle size; The adsorption pressure-gas flow rate main component particle size significant interactive coupling probability constraint vector point is added with the adsorption pressure-gas flow rate main component particle size significant interactive coupling probability weight value, and the base 2 logarithmic value is calculated to obtain the adsorption pressure-gas flow rate main component particle size significant interactive coupling information interaction vector: ;in, represents the probability constraint vector of significant interaction coupling between adsorption pressure and gas flow rate principal component particle size, represents the probability weight value of significant interaction coupling between adsorption pressure and gas flow rate principal component particle size, Indicates adding by position point. The interaction vector representing the significant interaction coupling information of adsorption pressure-gas flow rate principal component particle size; After subtracting the adsorption pressure-gas flow rate main component particle size significant interactive coupling probability constraint vector from the adsorption pressure-gas flow rate main component particle size significant interactive coupling probability weight value, the inverse of each eigenvalue is calculated to obtain the adsorption pressure-gas flow rate main component particle size significant interactive coupling sequence constraint vector: ;in, represents the probability constraint vector of significant interaction coupling between adsorption pressure and gas flow rate principal component particle size, represents the probability weight value of significant interaction coupling between adsorption pressure and gas flow rate principal component particle size, It means to subtract by position point; Represents the constraint vector of the significant interaction coupling sequence between adsorption pressure and gas flow rate principal component particle size; The adsorption pressure-gas flow rate main component particle size significant interaction coupling information interaction vector point is added to the adsorption pressure-gas flow rate main component particle size significant interaction coupling sequence constraint vector to obtain an optimized adsorption pressure-gas flow rate main component particle size significant interaction coupling feature vector.

[0047] Finally, the optimized adsorption pressure-gas flow rate principal component particle size significant interaction coupling feature vector is input into a classifier-based control optimizer to obtain the optimization instruction.

[0048] The above optimization process simulates the mesoscale interaction structure under probability constraints between the eigenvalue scale and the eigenvector scale of the adsorption pressure-gas flow main component granularity significant interactive coupling eigenvector through the probability statistical characteristics of the adsorption pressure-gas flow main component granularity significant interactive coupling eigenvector, so as to construct a bidirectional hidden variable motif based on the mesoscale short sequence relative to the probability statistical value to perform mesoscale bidirectional migration, and perform posterior recovery based on the sequence constraint of the interactive information, so as to improve the convergence effect of the adsorption pressure-gas flow main component granularity significant interactive coupling eigenvector under the probability density domain, and improve the accuracy of the optimization instruction obtained by the input classifier-based control optimizer. In this way, the adsorption pressure time series data and the gas flow time series data can be collected and analyzed in real time to better understand the dynamic changes and fluctuations of the time series interaction between the adsorption pressure and the gas flow in the adsorption process, so as to achieve corresponding dynamic adjustments, thereby improving the automation level of the pressure swing adsorption process control.

[0049] In step S4, another part of the gas after the conversion treatment is subjected to heat recovery, acid removal and pressure swing adsorption to obtain carbon monoxide. Specifically, Figure 7 The present invention is a flow chart of a method for producing ethylene glycol using raw coal gas according to an embodiment of the present application, wherein another part of the gas after the conversion treatment is subjected to heat recovery, acid removal and pressure swing adsorption to obtain carbon monoxide. Figure 7 As shown, another part of the gas after the conversion treatment is subjected to heat recovery, acid depuration and pressure swing adsorption to obtain carbon monoxide, including: S41: performing heat recovery treatment on another part of the gas after the conversion treatment to obtain heat-recovered gas; S42: performing the low-temperature methanol washing treatment on the heat-recovered gas to obtain low-temperature acid depuration gas; S43: performing the pressure swing adsorption on the low-temperature acid depuration gas to obtain the carbon monoxide.

[0050] In step S41, another part of the gas after the conversion treatment is subjected to heat recovery treatment to obtain heat-recovered gas. It should be understood that the high-temperature gas after the conversion treatment contains a large amount of thermal energy. In order to fully recycle the thermal energy in the high-temperature gas and improve the energy efficiency of the entire production process, another part of the gas after the conversion treatment can be subjected to heat recovery treatment. Specifically, the high-temperature gas can be passed through a low-pressure waste heat boiler, a desalted water preheater, and a water cooler in sequence. In this way, not only can the heat in the high-temperature gas be effectively recovered, but the gas temperature can also be reduced to a suitable level to provide suitable operating conditions for subsequent processes.

[0051] In step S42, the gas after the heat recovery treatment is subjected to the low-temperature methanol washing treatment to obtain the low-temperature acid-purified gas. It should be understood that in order to efficiently remove acidic gases from the heat recovery gas, these acidic gases will not only pollute the environment, but may also damage the catalyst and equipment in the subsequent process. Therefore, it is necessary to perform the low-temperature methanol washing treatment on the gas after the heat recovery treatment. Through the low-temperature methanol washing treatment, impurities in the gas after the heat recovery treatment are removed, which is beneficial to protecting the catalyst and equipment and reducing the harm to the environment.

[0052] In step S43, the pressure swing adsorption is performed on the gas after the low-temperature acid depuration to obtain the carbon monoxide. It should be understood that the pressure swing adsorption process utilizes the characteristic that the adsorption amount of different components of the adsorbent used varies with the pressure, so as to separate the strongly adsorbed components from the weakly adsorbed components. Under a certain adsorption pressure, the gas after the low-temperature acid depuration enters the adsorption tower, and the strongly adsorbed components with larger adsorption capacity are adsorbed and retained in the bed, while the weakly adsorbed components with smaller adsorption capacity are output from the outlet of the bed. The adsorbent saturated with adsorption is desorbed and regenerated by steps such as decompression, flushing and evacuation. When the bed is saturated with adsorption, the raw gas inlet valve and the product gas outlet valve are closed, and the input of raw gas is stopped to complete the whole process. Here, by performing pressure swing adsorption treatment on the gas after the low-temperature acid depuration, the CO component can be extracted, and the effective separation of CO and other components is achieved.

[0053] In step S5, after the carbon monoxide is pressurized, the pressurized carbon monoxide and the hydrogen are passed into an ethylene glycol device to obtain the ethylene glycol. It should be understood that pressurizing the carbon monoxide can increase the concentration of carbon monoxide, thereby improving the rate and selectivity of the chemical reaction. In the process of synthesizing ethylene glycol, carbon monoxide and hydrogen are the main raw materials. By passing the pressurized carbon monoxide and hydrogen into an ethylene glycol device, ethylene glycol can be efficiently produced under suitable reaction conditions.

[0054] In summary, the method for producing ethylene glycol using raw coal gas based on the embodiment of the present application is explained, which first collects the time queue data of the adsorption pressure value and the gas flow value, and uses the data analysis and processing technology based on deep learning to perform the time series correlation of the adsorption pressure value and the gas flow value, so as to automatically judge whether the adsorption pressure value at the next time point needs to be increased according to the principal component significant interaction between the adsorption pressure value time series correlation feature and the gas flow value time series correlation feature. In this way, the adsorption pressure time series data and the gas flow time series data can be collected and analyzed in real time to better understand the dynamic changes and fluctuations of the time series interaction between the adsorption pressure and the gas flow during the adsorption process, so as to achieve corresponding dynamic adjustments, thereby improving the automation level of the pressure swing adsorption process control.

[0055] Figure 8 FIG. 1 is a block diagram of a system for producing ethylene glycol using raw coal gas according to an embodiment of the present application. Figure 8 As shown, according to the system 100 for producing ethylene glycol using raw coal gas in accordance with an embodiment of the present application, the system includes: a compression processing module 110, for compressing the raw raw coal gas introduced to obtain compressed raw coal gas; a conversion processing module 120, for converting the compressed raw coal gas to obtain converted gas; a hydrogen extraction module 130, for performing conversion treatment, acid removal and pressure swing adsorption on a part of the converted gas to obtain hydrogen; a carbon monoxide extraction module 140, for performing heat recovery, acid removal and pressure swing adsorption on another part of the converted gas to obtain carbon monoxide; and an ethylene glycol synthesis module 150, for pressurizing the carbon monoxide and then passing the pressurized carbon monoxide and the hydrogen into an ethylene glycol device to obtain the ethylene glycol.

[0056] Here, those skilled in the art will appreciate that the specific functions and operations of the various modules in the system 100 for producing ethylene glycol using raw coal gas have been described in detail above. Figures 1 to 7 The method for producing ethylene glycol using raw coal gas has been described in detail, and therefore, its repeated description will be omitted.

[0057] In summary, the system 100 for producing ethylene glycol using raw coal gas based on the embodiment of the present application is explained, which first collects the time queue data of the adsorption pressure value and the gas flow value, and uses the data analysis and processing technology based on deep learning to perform the time series correlation of the adsorption pressure value and the gas flow value, so as to automatically judge whether the adsorption pressure value at the next time point needs to be increased according to the principal component significant interaction between the adsorption pressure value time series correlation feature and the gas flow value time series correlation feature. In this way, the adsorption pressure time series data and the gas flow time series data can be collected and analyzed in real time to better understand the dynamic changes and fluctuations of the time series interaction between the adsorption pressure and the gas flow during the adsorption process, so as to achieve corresponding dynamic adjustments, thereby improving the automation level of the pressure swing adsorption process control.

[0058] In the process of producing ethylene glycol, the raw coal gas is compressed, converted, transformed, acid-stripped, and PSA CO and hydrogen to obtain qualified carbon monoxide and hydrogen as the raw material for downstream ethylene glycol production. Specifically, after compression and conversion, one stream of the raw coal gas is converted, acid-stripped, purified, and then PSA hydrogenated to obtain H2; the other stream is heat-recovered, acid-stripped, purified, and then PSA CO stripped to obtain carbon monoxide. The permeate gas of PSA CO stripping enters PSA hydrogen stripping together with the above-mentioned conversion purified gas to obtain H2. After carbon monoxide is pressurized, it is sent to the ethylene glycol unit separately with hydrogen, and finally ethylene glycol is produced.

[0059] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details of the above application are only for the purpose of illustration and ease of understanding, rather than limitation, and the above details do not limit the present application to the use of the above specific details to be implemented.

[0060] In this application, words such as "including", "comprising", "having", etc. are open words, meaning "including but not limited to", and can be used interchangeably therewith. The words "or" and "and" used herein mean the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein means the phrase "such as but not limited to", and can be used interchangeably therewith.

[0061] It should also be noted that in the system and method of the present application, each module or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present application.

[0062] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features of the present application.

[0063] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for producing ethylene glycol using raw coal gas, characterized in that: include: S1: compressing the raw raw coal gas to obtain compressed raw coal gas; S2: converting the compressed raw coal gas to obtain converted gas; S3: performing shift treatment, acid removal purification and pressure swing adsorption on a portion of the gas after the conversion treatment to obtain hydrogen; S4: performing heat recovery, acid removal and pressure swing adsorption on another part of the gas after the conversion treatment to obtain carbon monoxide; S5: After the carbon monoxide is pressurized, the pressurized carbon monoxide and the hydrogen are introduced into an ethylene glycol device to obtain the ethylene glycol.

2. The method for producing ethylene glycol using raw coal gas according to claim 1, characterized in that: The compressed raw coal gas is subjected to conversion treatment to obtain converted gas, comprising: Performing non-catalytic partial oxidation treatment on the compressed raw coal gas to obtain oxidized raw coal gas; The raw coal gas after oxidation treatment is subjected to high temperature conversion reaction to obtain the converted gas.

3. The method for producing ethylene glycol using raw coal gas according to claim 2, characterized in that: A portion of the gas after the conversion treatment is subjected to shift treatment, acid removal purification and pressure swing adsorption to obtain hydrogen, including: Performing a shift treatment on a portion of the converted gas to obtain a shifted gas; Performing low-temperature methanol washing on the gas after the conversion treatment to obtain acid-depurated gas; The acid-purified gas is subjected to pressure swing adsorption to obtain the hydrogen.

4. The method for producing ethylene glycol using raw coal gas according to claim 3, characterized in that: The gas after acid removal and purification is subjected to pressure swing adsorption to obtain the hydrogen, comprising: Obtaining a time queue of adsorption pressure values ​​collected by a pressure sensor; Obtaining a time queue of gas flow values ​​collected by a flow sensor; Performing sequence encoding on the time queue of the adsorption pressure value and the time queue of the gas flow value to obtain an adsorption pressure time series associated feature vector and a gas flow time series associated feature vector; The adsorption pressure time series correlation characteristic vector and the gas flow time series correlation characteristic vector are subjected to adsorption pressure-gas flow interactive response coupling to obtain the adsorption pressure-gas flow main component granularity significant interactive coupling characteristics, including: performing principal component analysis on the adsorption pressure time series correlation characteristic vector and the gas flow time series correlation characteristic vector to obtain a set of adsorption pressure time series correlation main component characteristic components and a set of gas flow time series correlation main component characteristic components; performing optimal interactive matching coupling on the set of adsorption pressure time series correlation main component characteristic components and the set of gas flow time series correlation main component characteristic components to obtain the adsorption pressure-gas flow main component granularity significant interactive coupling characteristics; Based on the significant interactive coupling characteristics of the adsorption pressure-gas flow rate main component particle size, an optimization instruction is obtained, and the optimization instruction is used to indicate whether the adsorption pressure value at the next time point needs to be increased.

5. The method for producing ethylene glycol using raw coal gas according to claim 4, characterized in that: The time queue of the adsorption pressure values ​​and the time queue of the gas flow values ​​are sequence-encoded to obtain an adsorption pressure time series correlation feature vector and a gas flow time series correlation feature vector, including: inputting the time queue of the adsorption pressure values ​​and the time queue of the gas flow values ​​into a sequence encoder based on a forward LSTM model to obtain the adsorption pressure time series correlation feature vector and the gas flow time series correlation feature vector.

6. The method for producing ethylene glycol using raw coal gas according to claim 5, characterized in that: The optimal interactive matching coupling is performed on the set of the principal component characteristic components associated with the adsorption pressure time series and the set of the principal component characteristic components associated with the gas flow time series to obtain the significant interactive coupling characteristics of the adsorption pressure-gas flow principal component granularity, including: Taking each adsorption pressure time series associated principal component characteristic component in the set of adsorption pressure time series associated principal component characteristic components as a query vector and taking the set of gas flow time series associated principal component characteristic components as a query library, matching the gas flow time series associated principal component characteristic components that best match each of the query vectors from the query library to obtain a set of best matching pairs of {adsorption pressure time series associated principal component characteristic components; gas flow time series associated principal component characteristic components}; Input each best matching pair of {adsorption pressure time series associated principal component characteristic component; gas flow time series associated principal component characteristic component} in the set of best matching pairs of {adsorption pressure time series associated principal component characteristic component; gas flow time series associated principal component characteristic component} into a multi-scale interactive response coupling module to obtain a set of multi-scale interactive coupling representation vectors of adsorption pressure-gas flow best matching pairs; The set of multi-scale interaction coupling representation vectors of the adsorption pressure-gas flow rate best match is cascaded to obtain the adsorption pressure-gas flow rate main component particle size significant interaction coupling feature vector as the adsorption pressure-gas flow rate main component particle size significant interaction coupling feature.

7. The method for producing ethylene glycol using raw coal gas according to claim 6, characterized in that: The {adsorption pressure time series is associated with the principal component characteristic component; Each {adsorption pressure time series associated principal component characteristic component} in the set of the best matching pairs; The gas flow time series correlation principal component characteristic component} best match pair is input into the multi-scale interactive response coupling module to obtain a set of multi-scale interactive coupling representation vectors of the adsorption pressure-gas flow best match pair, including: Respectively calculating the L2 norms of the adsorption pressure time series associated principal component characteristic component and the gas flow time series associated principal component characteristic component in the best matching pair of {adsorption pressure time series associated principal component characteristic component; gas flow time series associated principal component characteristic component} to obtain the adsorption pressure norm value and the gas flow norm value; Extracting a maximum value from the adsorption pressure norm value, the gas flow norm value and the modulation parameter to obtain an interaction maximum value; Respectively calculating the positional subtraction, positional dot product and positional addition between the characteristic components of the adsorption pressure time series associated principal component and the characteristic components of the gas flow time series associated principal component to obtain an adsorption pressure-gas flow time series associated principal component difference vector, an adsorption pressure-gas flow time series associated principal component dot product vector and an adsorption pressure-gas flow time series associated principal component sum vector; Cascade processing is performed on the adsorption pressure-gas flow time series associated principal component difference vector, the adsorption pressure-gas flow time series associated principal component dot product vector and the adsorption pressure-gas flow time series associated principal component sum vector to obtain an adsorption pressure-gas flow time series associated principal component cascade vector; Performing one-dimensional convolution coding on the adsorption pressure-gas flow time series correlation principal component cascade vector to obtain an adsorption pressure-gas flow time series correlation principal component convolution coding vector; The adsorption pressure-gas flow time series associated principal component convolution encoding vector is subjected to local window-based maximum pooling processing, and the adsorption pressure-gas flow time series interaction representation vector obtained is divided point by point by the interaction maximum value to obtain the multi-scale interaction coupling representation vector of the adsorption pressure-gas flow best match.

8. The method for producing ethylene glycol using raw coal gas according to claim 7, characterized in that: Based on the adsorption pressure-gas flow rate main component particle size significant interactive coupling characteristics, an optimization instruction is obtained, including: inputting the adsorption pressure-gas flow rate main component particle size significant interactive coupling feature vector into a classifier-based control optimizer to obtain the optimization instruction.

9. The method for producing ethylene glycol using raw coal gas according to claim 8, characterized in that: The other part of the gas after the conversion treatment is subjected to heat recovery, acid removal and pressure swing adsorption to obtain carbon monoxide, comprising: Performing heat recovery treatment on another part of the converted gas to obtain heat recovered gas; Performing the low-temperature methanol washing treatment on the gas after the heat recovery treatment to obtain the low-temperature acid-depurated gas; The low-temperature acid-depurated gas is subjected to pressure swing adsorption to obtain the carbon monoxide.

10. A system for producing ethylene glycol using raw coal gas, characterized in that: include: A compression processing module, used for compressing the raw raw coal gas introduced to obtain compressed raw coal gas; A conversion processing module, used for performing conversion processing on the compressed raw coal gas to obtain converted gas; A hydrogen extraction module, used for performing conversion treatment, acid removal purification and pressure swing adsorption on a portion of the gas after the conversion treatment to obtain hydrogen; A carbon monoxide extraction module, for performing heat recovery, acid removal and pressure swing adsorption on another part of the gas after the conversion treatment to obtain carbon monoxide; The ethylene glycol synthesis module is used to pressurize the carbon monoxide and then pass the pressurized carbon monoxide and the hydrogen into an ethylene glycol device to obtain the ethylene glycol.

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