Intrinsic safety optimization design method of complex rectification system based on mass energy level

Abstract

The invention belongs to the field of computer-aided complex rectification system optimization design technology and chemical process safety, and discloses an intrinsic safety optimization design method of a complex rectification system based on mass energy level. By constructing a nonlinear programming model based on a mass energy level, an optimal configuration structure with both safety and economy is designed for a complex rectification system. The optimization target is to determine the optimal configuration structure by balancing the total annual cost and intrinsic safety of the complex rectification system. According to the method, the mass energy level is used as the minimum unit of the complex rectification system, so that the understanding space range is expanded, and the risk that a suboptimal configuration structure is misjudged to be optimal is effectively avoided. In addition, key variables in the rectification simple model are associated with fire explosion indexes, and essential safety and total annual cost are both considered. A reliable and efficient solution is provided for intrinsic safety optimization design of a complex rectification system, and the method is suitable for multi-objective optimization requirements in practical engineering.

Description

Technical Field

[0001] The present invention relates to computer-aided optimization design technology for complex distillation systems and the field of chemical process safety, and in particular to an intrinsically safe optimization design method for complex distillation systems based on mass-energy levels. Background Art

[0002] In the petrochemical industry, distillation is a widely used material separation process, mainly used for the separation and purification of hazardous chemicals, and its energy consumption accounts for more than 50% of the total production energy consumption. As an advanced distillation technology, complex distillation has significant advantages over traditional distillation in terms of separation efficiency, energy consumption, flexibility, product quality, process safety, adaptation to new technologies and economy. Complex distillation significantly improves separation efficiency through the design of multi-tower or multi-phase systems, while optimizing design and operating conditions to reduce energy consumption. In addition, the technology shows greater flexibility and can effectively respond to changes in different feed components to meet the market demand for high-purity products.

[0003] Although complex distillation technology has significant advantages in multi-component separation, its practical application faces challenges such as high complexity of design and operation, high initial investment, and strict requirements for equipment and processes, which greatly increase the difficulty of process management and optimization. To address these problems, Zou et al. introduced the concepts of super tower and mass energy level in "Study on Comprehensive Methods of Multi-component Thermally Coupled Distillation Separation System", which fundamentally simplified the separation process of multi-component non-azeotropic mixtures, reduced the complexity and cost of the separation system, and improved the separation efficiency. To further optimize process management, Zhu et al. optimized the extractive distillation process through parallel genetic algorithm in "Research Progress and Prospects of Intrinsic Safety Assessment Methods for Chemical Processes", which not only maximized the net income but also significantly reduced the annual total cost. At the same time, Sun et al. proposed an extractive distillation scheme combining heat integration and steam recompression heat pump in "An intensified energy-saving architecture for side-stream extractive distillation of four-azeotrope mixtures considering economic, environmental and safety criteria simultaneously", which further simplified the process flow and achieved significant cost reduction. These research results significantly reduce the difficulty of management and optimization of complex distillation, providing effective solutions for economical and efficient separation processes.

[0004] However, when the petrochemical industry pursues low-energy, low-cost and high-safety separation technologies, process safety is always the core issue. The conceptual design stage usually pays too much attention to economic and environmental factors and ignores safety considerations. This leads to the need for additional control and protection measures for the safety design of complex distillation systems, which increases the complexity of the system, and these safety devices can often only mitigate the consequences of accidents, but cannot effectively prevent accidents. To this end, Vázquez et al. combined the Deb algorithm with principal component analysis in "Multi-objective Early Design of Complex Distillation Sequences Considering Economic and Inherent Safety Criteria", directly incorporating intrinsic safety into the design of distillation towers, and achieved an economically feasible and intrinsically safe optimization design. Medina et al. emphasized the introduction of safety and controllability in the early stages of process design in "A new index for chemical process design considering risk analysis and controllability Symposium", and achieved a safer intrinsically safe process through new indicators, demonstrating an important shift in the incorporation of safety into process design.

[0005] In addition, Zhu et al. integrated intrinsic safety, environmental and economic factors to optimize the design in the conceptual design stage of distillation for the production of propylene glycol monomethyl ether acetate in “Development of novel hybrid pre-separation / extractive reactive distillation processes for the separation of methanol / methylacetate / ethyl acetate”. Ye et al. developed a comprehensive framework for intrinsic safety of thermally coupled distillation sequences in “An inherently safer development approach for thermally coupled distillation sequences: Application inhazardous chemical separation”, which effectively integrated and optimized safety considerations and evaluated the intrinsic safety of each tower configuration using the state task network (STN) and the Dow Fire and Explosion Index (F&EI). However, the existing intrinsic safety optimization design of complex distillation systems mainly focuses on distillation tower sections and sequences, which leads to an incomplete optimization solution space, which may result in suboptimal configurations, increased risks and additional costs.

[0006] In order to solve the problem of intrinsic safety optimization design of complex distillation systems, this study regards the mass-energy level as the basic unit of complex distillation systems, aiming to explore the mutual connection between gas-liquid phase streams at various stages, so as to design a complex distillation system that considers intrinsic safety and economic factors. In this context, the implementation of intrinsic safety optimization design methods in complex distillation systems still faces two major challenges: First, existing studies often use tower sections or distillation sequences as system units, resulting in an incomplete optimization solution space, which may produce suboptimal configurations, increase the redundancy of safety facilities in actual designs, and further increase risks and cost budgets. Secondly, on the basis of establishing a simple distillation model, it is also a major challenge to achieve the coupling of system safety and economy. It is difficult to select a suitable intrinsic safety index to quantify the safety of complex distillation systems and analyze the solved optimal configuration structure. Therefore, determining the research unit of complex distillation and effectively coupling safety and economic factors have become issues that need to be solved urgently. Summary of the invention

[0007] In order to solve the above-mentioned existing problems and two challenges, the purpose of the present invention is to propose a method for optimizing the intrinsic safety of a complex distillation system based on mass-energy level. A nonlinear programming (NLP) model is used to optimize the total annual cost (TAC) and intrinsic safety index (ISI) of the complex distillation system. The ε-constraint method is used to solve the Pareto solution in the process, thereby obtaining the structure-performance relationship between the intrinsic safety and economy of the complex distillation system to obtain the optimal configuration structure of the system.

[0008] The technical solution of the present invention is as follows: an intrinsically safe optimization design method for a complex distillation system based on mass-energy level, comprising the following steps:

[0009] Step 1: Constrain the mass-energy level MES through the MESH equation; construct the state space superstructure through the coupling between the distribution network DN and the cascade operation operator SCPO;

[0010] The MESH equations are material balance, gas-liquid phase balance, additive balance, and energy balance;

[0011] The mass-energy level MES is the basic building block in a complex distillation system, and the mass-energy level includes material separation, material transfer, and heat exchange;

[0012] The distribution network DN is responsible for the distribution of materials and heat and is a key component in a complex distillation system. The feed FEED enters from the left side of the distribution network DN and is distributed to each mass-energy level and the mixer before the product stream through the stream distributor as needed;

[0013] The cascade operation operator SCPO includes all mass-energy level units in the distillation system, wherein any two mass-energy levels are connected by distributors and mixers arranged on the distribution network DN;

[0014] The state space superstructure is formed by the coupling between the distribution network DN and the cascade operation operator SCPO; in the state space superstructure, all mass and energy levels are connected through the distribution network DN and the cascade operation operator SCPO, so as to realize the material and heat exchange between different streams; based on the state space superstructure, the economic performance of the complex distillation system is quantified;

[0015] The economics of complex distillation systems are quantified using the total annualized cost (TAC): The general equation (1) represents the total annualized cost of a complex distillation system, which is the equipment cost ec and operating costs oc sum:

[0016] TAC = cost ec +cost oc (1)

[0017] Step 2: Select F&EI as the intrinsic safety index for intrinsic safety optimization and quantify the intrinsic safety of complex distillation systems; F&EI is one of the most widely used safety indicators in the process industry. Its wide application is mainly due to its ability to effectively estimate the potential losses of fire and explosion and provide a reliable basis for the implementation of control measures. In addition, F&EI is not only a quantitative risk analysis method, but also plays a vital role in hazard identification at the unit level, supporting inherently safer designs. Therefore, this study selects F&EI as the intrinsic safety index for safety optimization of complex distillation systems.

[0018] The general equation (2) shows that F&EI is the product of the material factor MF, the general process hazard factor F1 and the special process hazard factor F2:

[0019] F&EI=MF·F1·F2 (2)

[0020] Step 3: Based on the state space superstructure, the economy of the complex distillation system is optimized, and the intrinsic safety index F&EI is used to optimize the intrinsic safety of the complex distillation system. The complex distillation configuration structure is designed. The objective functions are to minimize the total annual cost TAC and minimize F&EI of the complex distillation system. In order to minimize TAC and F&EI, nonlinear programming NLP is used to perform multi-objective optimization on the complex distillation system. According to the actual conditions of the distillation process, the upper and lower limits of the optimization variables are specified to obtain the structure-activity relationship between the intrinsic safety and economy of the complex distillation system, and the optimal configuration structure of the system is obtained. The NLP model formula is as follows:

[0021] Objective function: obj1 = minTAC; obj2 = minF&EI;

[0022] In the above general equation, obj1 and obj2 are the objective functions;

[0023] Step 4: Use GAMS solver to solve the NLP model, and obtain the most economical and safest complex distillation configuration structures respectively; obtain the value range of F&EI from the most economical configuration structure and the safest configuration structure; add constraints on the value of F&EI to the NLP model, and then solve it in GAMS to finally obtain a series of Pareto solutions; if no solution that meets the constraints is obtained, return to step 3 to relax the constraint range;

[0024] Step 5: Select a qualified solution from the Pareto solution set as the optimal feasible solution for the implementation case, wherein the optimal feasible solution meets both the industrial economic feasibility and the intrinsic safety feasibility of the complex distillation system; the intrinsic safety feasibility is achieved through F&EI reduction, which means that in the process of solving the problem, the complex distillation system in the medium or high risk range is gradually reduced to the low risk range.

[0025] The basic building unit distillation tower plate in the complex distillation system serves as a mass-energy level, and each distillation tower plate serves as a separate heat transfer unit and mass transfer unit. Due to the complex gas-liquid phase flow interaction between the distillation tower plate units, the distillation tower plates presented are not connected sequentially.

[0026] The most economical and safest complex distillation configuration was constructed based on the following assumptions and simplifications:

[0027] 1) The separation process only uses distillation technology;

[0028] 2) Only steady-state operation is considered during the separation process;

[0029] 3) Since the Antoine equation is used for phase equilibrium calculation, the system only considers the separation of ideal and near-ideal mixtures;

[0030] 4) The system is a mixture system composed of multiple components, which has azeotropic properties and will be processed into N different products;

[0031] 5) The system is fed at the bubble point;

[0032] 6) To optimize the inherent safety and total annual cost of the system to obtain an optimal distillation configuration structure while meeting specific product requirements.

[0033] The equipment cost cost of the complex distillation system in the objective function obj1 ec and operating costs oc The specific expression is equations (3) to (4):

[0034] costec =f(NT,D n ,Ar cod,n ,Ar reb,n ) (3)

[0035] cost oc =C w ∑Q cod,n σ n +C h ∑Q reb,n σ n (4)

[0036] In equation (3), cost ec It includes the number of plates NT and the diameter of the plate D n , Reboiler area Ar cod,n and condenser area Ar reb,n The correlation function of

[0037] The operating cost in equation (4) is oc Considering the heat of the reboiler and condenser, C w and C h Respectively represent the unit price of cold utility engineering and the unit price of hot utility engineering, σ n is a binary variable describing the existence of heat exchanger n, Q cod,n and Q reb,n They are the heat of the condenser and the heat of the reboiler respectively.

[0038] The physical and chemical parameters of the complex distillation system are: pressure P, bubble point temperature T c , constant pressure specific heat capacity C P ; The kinetic parameters of complex distillation systems are: Antoine constant A c , ambient temperature T0, molar flow rate, vaporization enthalpy h;

[0039] The mass-energy level unit is constrained by MESH equations; the constraint equations are MESH equations, which are material balance constraints, gas-liquid phase equilibrium constraints, additive equilibrium constraints, and energy balance constraints;

[0040] The overall material balance constraint of the complex distillation system is given by equation (5):

[0041]

[0042] The sum of the total feed molar flow rates of a complex distillation system is equal to the sum of the total product molar flow rates; k is the molar flow rate of feed k, pro s is the molar flow rate of product stream s, FEED is the feed distributor of the distribution network DN, DNMIX U It is a mixer for DN upper boundary products;

[0043] The mass-energy level material balance constraints are given by equations (6) to (12):

[0044]

[0045] Equation (6) is the mass balance constraint of the feed distributor on the left side of DN. The molar flow rate of feed k is distributed to each mass-energy level inlet mixer and each product pre-mixer; k,j is the flow rate from feed distributor k to mixer j, fedp k,j is the flow rate from feed distributor k to product mixer s, DNMIX R It is the DN right boundary mass level import mixer;

[0046] Equation (7) is the material balance constraint for the product mixer components on the upper side of DN. The product stream can come directly from the feed or from the output of a certain mass-energy level; mc s,c is the molar composition of component c in product mixer s, rf i,s is the molar flow rate from mass-energy level distributor i to product stream s, fedp k,s is the molar flow rate from feed distributor k to product mixer s, mc k,c is the molar composition of component c in feed distributor k, DNMIX L It is the DN lower boundary distributor;

[0047] Equation (8) is the material balance constraint for the pre-mixer, fs i is the total molar flow rate of stream i leaving the mass-energy level distributor, rf i,s is the molar flow rate from the mass-energy level distributor i to the product mixer s;

[0048] Equation (9) is the material balance constraint for the distributor on the lower side of DN, fs i is the total molar flow rate of stream i leaving the mass-energy level distributor;

[0049] Equation (10) is the material balance constraint of the mass-energy level distributor, fs n,q is the molar flow rate of phase q leaving MES stream n, mc n,q,c is the molar composition of stream component c in phase q in MES distributor n, fs n,i,q is the molar flow rate of stream component c of phase q from MES n to mass-energy level distributor i, mc n,i,q,c is the molar composition of stream component c in phase q from MES n to mass-energy level distributor i, rf n,n’,q is the molar flow rate of phase q from MES number n to MES number n', where n, n' are the numbers of the MES;

[0050] Equation (11) is the material balance constraint of the mixer on the mass-energy level, rf n,q is the molar flow rate of phase q in MES distributor n, rf n’,n,q is the molar flow rate of phase q from MES n' to MES n;

[0051] Equation (12) is the total material balance constraint for mass-energy level;

[0052] The gas-liquid phase equilibrium constraints of the complex distillation system are given by equations (13) to (14):

[0053]

[0054] Equation (13) is the gas-liquid phase equilibrium constraint of the complex distillation system, where mc n,qv,c is the molar composition of gaseous stream component c in MES distributor n, mc n,ql,c is the molar composition of liquid stream component c of MES distributor n, K n is the equilibrium constant of MES n;

[0055] Equation (14) is the Antoine constraint, P is the total pressure of the complex distillation system, T n is the temperature of MES n, A c , B c and C c are the Antoine constants of component c respectively;

[0056] The additive equilibrium constraint of the complex distillation system is given by equation (15):

[0057]

[0058] Equation (15) is an additive constraint, the sum of each component is equal to 1, mc q,c is the molar composition of component c in phase q;

[0059] The total heat balance constraint of the complex distillation system is given by equation (16):

[0060]

[0061] hfed k is the specific enthalpy of feed stream k, hpro s is the specific enthalpy of product stream s;

[0062] The total heat balance constraint of mass-energy level is given by equations (17) to (19):

[0063]

[0064] Equation (17) is the energy balance constraint for the mass-energy level pre-mixer, hfs jis the specific enthalpy of stream j in the pre-mixer of MES n = j, hfed k is the specific enthalpy of feed stream k;

[0065] Equation (18) is the energy balance constraint for the product mixer components on the upper side of DN, hrf n,q is the specific enthalpy of phase q in MES n, hrf n’,q is the specific enthalpy of phase q in MES n';

[0066] Equation (19) is the total energy balance constraint for mass-energy level, hfs n,q is the specific enthalpy of phase q in MES n;

[0067] The combined effect of the above constraint equations ensures the rationality and balance of the complex interactions between mass-energy levels.

[0068] The general process risk factor F1 and the special process risk factor F2 of the complex distillation system in the objective function obj2 are specifically expressed as equations (20) to (21):

[0069]

[0070] In equation (20), there are 6 general risk factors, including: exothermic reaction A, endothermic reaction B, material handling and transportation C, closed or indoor process unit D, channel E, emission and leakage control F;

[0071] In equation (21), the special process hazard factor F2 has 12 items, including: toxic substances A, negative pressure operation B, operation in or near the combustion range C, dust explosion D, release pressure E, low temperature F, the amount of flammable and unstable substances G, corrosion and abrasion H, leaking joints and packings I, the use of open flame equipment J, hot oil heat exchange system K, rotating equipment L;

[0072] By quantifying the intrinsic safety of the complex distillation system in F&EI, the variables in F&EI are: the penalty term G in F2; G refers to the amount of flammable and unstable substances in the complex distillation system, which is specifically given by equation (22):

[0073] log(G)≥0.17179+0.42988logTB-0.37244log(TB) 2 +0.17712log(TB) 3 -0.029984log(TB) 4 (twenty two)

[0074] In the above equation (22), TB is the heat of the entire distillation process.

[0075] The heat of the entire distillation process is the combustion enthalpy H in the system.C and the total inventory of flammable and unstable substances in the system n The correlation function is given by equation (23):

[0076] TB=f(inv n ,H c ) (twenty three)

[0077] The total inventory of flammable and unstable substances in the system n is the only variable in the calculation of F&EI, using 30% of the distillation column volume as inventory given by equation (24):

[0078] inv n =30%·vol n (twenty four)

[0079] The volume constraint of the distillation column is given by the plate area Ar n and the spacing H between the plates n The constraint is given by equation (25):

[0080] vol n =Ar n ·H n (25)

[0081] The plate area constraint of the distillation tower is given by equation (26):

[0082]

[0083] The plate area constraint equation and the mass-energy level constraint equation are obtained by calculating the gas phase molar flow rate rf in the nth MES distributor. n,qv By linking them together, the essential safety and economy of the complex distillation system are linked, so as to achieve the purpose of establishing the optimal configuration structure of the complex distillation system.

[0084] Specifically, step 4 includes establishing the proposed NLP model in GAMS and solving it using the CONOPT solver; using the ε-constraint method to perform multi-objective optimization on the NLP model; converting the multi-objective problem into a series of single-objective problems, and transferring other objectives to the constraints; and providing the following information in the optimization scheme: the optimized configuration structure of the complex distillation system; the TAC and F&EI of the complex distillation system; the heat of the heat exchanger Q n ; The quantity of flammable and unstable substances in F2 G; The total inventory of flammable and unstable substances inv n ; The molar flow rate and molar composition of gas and liquid phases between each mass and energy level.

[0085] Compared with the existing invention technology, the present invention has the following beneficial effects:

[0086] The present invention performs intrinsically safe design of complex distillation systems based on mass-energy levels, and faces two challenges in practical applications. First, the intrinsically safe optimization design of existing complex distillation systems usually takes tower sections or distillation sequences as research units. This method limits the completeness of the optimization solution space and easily leads to the emergence of suboptimal configuration structures. Suboptimal configuration structures may increase the redundancy of safety facilities in chemical design, which not only increases system risks but also leads to additional cost expenditures. Therefore, reasonably determining the research unit of a complex distillation system is the key to ensuring the quality of the optimization results.

[0087] Secondly, based on the simplified distillation model, achieving effective coupling between the intrinsic safety and economy of the system is another major challenge. How to select appropriate intrinsic safety indicators to quantify the safety of complex distillation systems and scientifically analyze and select the optimal configuration structure based on this is an important link to ensure that the system design goals are achieved.

[0088] In response to the above challenges, the present invention introduces a state space superstructure based on mass-energy levels, further refines the research unit of the complex distillation system, and takes the distillation plate as the basic research unit. Each plate can not only independently complete the mass transfer and heat transfer process, but also has input and extraction functions, and the connection method between plates is not restricted by a fixed sequence. This method greatly expands the scope of the understanding space and effectively improves the flexibility and completeness of the optimization results.

[0089] In addition, the present invention quantifies the intrinsic safety of the system through the fire and explosion index (F&EI), evaluates the safety using the system reserves, and associates the gas-liquid phase molar flow rate with the economic parameter, so that the system can achieve an organic combination of safety and economy in multi-objective optimization. Finally, the present invention proposes an optimization design method combining the mass-energy level state space superstructure and the intrinsic safety index for the intrinsic safety design of complex distillation systems, which significantly expands the safety range of the system design and provides an efficient solution suitable for practical engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 is the mass-energy level state space superstructure;

[0091] Figure 2 It is a flow chart of the intrinsic safety design method of the complex distillation system based on mass-energy level of the present invention;

[0092] Figure 3 The economically optimal configuration structure for the implementation case;

[0093] Figure 4 To provide the optimal configuration structure for economy and safety of the implementation case. DETAILED DESCRIPTION

[0094] The process and effects of the present invention are explained below with reference to the accompanying drawings and implementation examples.

[0095] According to this idea, an engineering example was used: the process of distillation optimization design of benzene-toluene-xylene (BTX) ternary non-azeotropic system to demonstrate the optimization value of the proposed method, thereby highlighting the versatility and effectiveness of the proposed intrinsically safe optimization design method for complex distillation systems based on mass-energy levels.

[0096] The method is applied to an engineering example: the separation of BTX ternary azeotropic aromatic mixture is mainly studied. In previous studies, a simple model of distillation has been established, and the physicochemical parameters and operating conditions are known. The following gives a detailed implementation method and specific operation process of the method, but the protection scope of the present invention is not limited to the following examples.

[0097] See also Figure 2 The present invention specifically discloses a complex distillation system intrinsic safety optimization design framework based on mass-energy level, including the following steps:

[0098] Step 1: Constrain the mass-energy level MES through the MESH equation; construct the state space superstructure through the coupling between the allocation network DN and the cascade operation operator SCPO; It is known from previous studies that this implementation case is an engineering example, and its physical and chemical parameters and operating conditions are known. Use the total annualized cost TAC to quantify the economic performance of the complex distillation system: The general equation (1) represents the total annualized cost of the complex distillation system, which is the equipment cost cost ec and operating costs oc sum:

[0099] TAC = cost ec +cost oc (1)

[0100] Step 2: Select F&EI as the intrinsic safety index for intrinsic safety optimization and quantify the intrinsic safety of the complex distillation system; General equation (2) shows that F&EI is the product of material factor MF, general process hazard factor F1 and special process hazard factor F2:

[0101] F&EI=MF·F1·F2 (2)

[0102] Step 3: Based on the state space superstructure, the economy of the complex distillation system is optimized, and the intrinsic safety index F&EI is used to optimize the intrinsic safety of the complex distillation system. The complex distillation configuration structure is designed. The objective functions are to minimize the total annual cost TAC and minimize F&EI of the complex distillation system. In order to minimize TAC and F&EI, nonlinear programming NLP is used to perform multi-objective optimization on the complex distillation system. According to the actual conditions of the distillation process, the upper and lower limits of the optimization variables are specified to obtain the structure-activity relationship between the intrinsic safety and economy of the complex distillation system, and the optimal configuration structure of the system is obtained. The NLP model formula is as follows:

[0103] Objective function: obj1 = minTAC; obj2 = minF&EI;

[0104] Step 4: Use GAMS solver to solve the NLP model, and obtain the most economical and safest complex distillation configuration structures respectively; obtain the value range of F&EI from the most economical configuration structure and the safest configuration structure; add constraints on the value of F&EI to the NLP model, and then solve it in GAMS to finally obtain a series of Pareto solutions; if no solution that meets the constraints is obtained, return to step 3 to relax the constraint range;

[0105] Step 5: Select a qualified solution from the Pareto solution set as the optimal feasible solution for the implementation case, which meets both the industrial economic feasibility and the intrinsic safety feasibility of the complex distillation system; the intrinsic safety feasibility is achieved through F&EI reduction, which means that in the process of solving the problem, the complex distillation system in the medium or high risk range is gradually reduced to the low risk range. As shown in Table 1.

[0106] Furthermore, in step 1, the basic building unit distillation tower plate in the complex distillation system is used as the mass-energy level, and each distillation tower plate is used as a separate heat transfer unit and mass transfer unit. Due to the complex gas-liquid flow interaction between the distillation tower plate units, the distillation tower plates presented are not connected sequentially.

[0107] Furthermore, the most economical and safest complex distillation configuration structure constructed in step 3 is based on the following assumptions and simplifications:

[0108] 1) The separation process only uses distillation technology;

[0109] 2) Only steady-state operation is considered during the separation process;

[0110] 3) Since the Antoine equation is used for phase equilibrium calculation, the system only considers the separation of ideal and near-ideal mixtures;

[0111] 4) The system is a mixture system composed of N components, has azeotropic properties, and will be processed into N different products;

[0112] 5) The system feeds at the bubble point.

[0113] 6) This optimization method aims to optimize the inherent safety and total annualized cost of the system under the premise of meeting specific product requirements and obtain an optimal distillation configuration structure.

[0114] Furthermore, the intrinsically safe optimization design method for complex distillation systems based on mass-energy levels described in step 1 is characterized in that the equipment cost cost of the complex distillation system in the objective function obj1 is ec and operating costs oc The specific expression is equations (3) to (4):

[0115] cost ec =f(NT,D n ,Ar cod,n ,Ar reb,n ) (3)

[0116] cost oc =C w ΣQ cod,n σ n +C h ΣQ reb,n σ n (4)

[0117] In equation (3), cost ec It includes the number of plates NT and the diameter of the plate D n , Reboiler area Ar cod,n and condenser area Ar reb,n The correlation function of

[0118] The operating cost in equation (4) is oc Considering the heat of the reboiler and condenser, C w and C h Respectively represent the unit price of cold utility engineering and the unit price of hot utility engineering, σ n is a binary variable describing the existence of heat exchanger n, Q cod,n and Q reb,n They are the heat of the condenser and the heat of the reboiler respectively.

[0119] Furthermore, the physicochemical parameters of the complex distillation system described in step 1 or 2 are: pressure P, bubble point temperature T c , constant pressure specific heat capacity C P ; The kinetic parameters of complex distillation systems are: Antoine constant A c, ambient temperature T0, molar flow rate, vaporization enthalpy h;

[0120] The results are shown in Table 1 below:

[0121]

[0122] The mass-energy level unit is constrained by MESH equations; the constraint equations are MESH equations, which are material balance constraints, gas-liquid phase equilibrium constraints, additive equilibrium constraints, and energy balance constraints;

[0123] The overall material balance constraint of the complex distillation system is given by equation (5):

[0124]

[0125] The sum of the total feed molar flow rates of a complex distillation system is equal to the sum of the total product molar flow rates; k is the molar flow rate of feed k, pro s is the molar flow rate of product stream s, FEED is the feed distributor of the distribution network DN, DNMIX U It is a mixer for DN upper boundary products;

[0126] The mass-energy level material balance constraints are given by equations (6) to (12):

[0127]

[0128] Equation (6) is the mass balance constraint of the feed distributor on the left side of DN. The molar flow rate of feed k is distributed to each mass-energy level inlet mixer and each product pre-mixer; k,j is the flow rate from feed distributor k to mixer j, fedp k,j is the flow rate from feed distributor k to product mixer s, DNMIX R It is the DN right boundary mass level import mixer;

[0129] Equation (7) is the material balance constraint for the product mixer components on the upper side of DN. The product stream can come directly from the feed or from the output of a certain mass-energy level; mc s,c is the molar composition of component c in product mixer s, rf i,s is the molar flow rate from mass-energy level distributor i to product stream s, fedp k,s is the molar flow rate from feed distributor k to product mixer s, mc k,c is the molar composition of component c in feed distributor k, DNMIX L It is the DN lower boundary distributor;

[0130] Equation (8) is the material balance constraint for the pre-mixer, fs iis the total molar flow rate of stream i leaving the mass-energy level distributor, rf i,s is the molar flow rate from the mass-energy level distributor i to the product mixer s;

[0131] Equation (9) is the material balance constraint for the distributor on the lower side of DN, fs i is the total molar flow rate of stream i leaving the mass-energy level distributor;

[0132] Equation (10) is the material balance constraint of the mass-energy level distributor, fs n,q is the molar flow rate of phase q leaving MES stream n, mc n,q,c is the molar composition of stream component c in phase q in MES distributor n, fs n,i,q is the molar flow rate of stream component c of phase q from MES n to mass-energy level distributor i, mc n,i,q,c is the molar composition of stream component c in phase q from MES n to mass-energy level distributor i, rf n,n’,q is the molar flow rate of phase q from MES number n to MES number n', where n, n' are the numbers of the MES;

[0133] Equation (11) is the material balance constraint of the mixer on the mass-energy level, rf n,q is the molar flow rate of phase q in MES distributor n, rf n’,n,q is the molar flow rate of phase q from MES n' to MES n;

[0134] Equation (12) is the total material balance constraint for mass-energy level;

[0135] The gas-liquid phase equilibrium constraints of the complex distillation system are given by equations (13) to (14):

[0136]

[0137] Equation (13) is the gas-liquid phase equilibrium constraint of the complex distillation system, where mc n,qv,c is the molar composition of gaseous stream component c in MES distributor n, mc n,ql,c is the molar composition of liquid stream component c of MES distributor n, K n is the equilibrium constant of MES n;

[0138] Equation (14) is the Antoine constraint, P is the total pressure of the complex distillation system, T n is the temperature of MES n, A c , B c and C c are the Antoine constants of component c respectively;

[0139] The additive equilibrium constraint of the complex distillation system is given by equation (15):

[0140]

[0141] Equation (15) is an additive constraint, the sum of each component is equal to 1, mc q,c is the molar composition of component c in phase q;

[0142] The total heat balance constraint of the complex distillation system is given by equation (16):

[0143]

[0144] hfed k is the specific enthalpy of feed stream k, hpro s is the specific enthalpy of product stream s;

[0145] The total heat balance constraint of mass-energy level is given by equations (17) to (19):

[0146]

[0147]

[0148] Equation (17) is the energy balance constraint for the mass-energy level pre-mixer, hfs j is the specific enthalpy of stream j in the pre-mixer of MES n = j, hfed k is the specific enthalpy of feed stream k;

[0149] Equation (18) is the energy balance constraint for the product mixer components on the upper side of DN, hrf n,q is the specific enthalpy of phase q in MES n, hrf n’,q is the specific enthalpy of phase q in MES n';

[0150] Equation (19) is the total energy balance constraint for mass-energy level, hfs n,q is the specific enthalpy of phase q in MES n;

[0151] The combined effect of the above constraint equations ensures the rationality and balance of the complex interactions between mass-energy levels.

[0152] The general process risk factor F1 and the special process risk factor F2 of the complex distillation system in the objective function obj2 are specifically expressed as equations (20) to (21):

[0153]

[0154] In equation (20), there are 6 general risk factors, including: exothermic reaction A, endothermic reaction B, material handling and transportation C, closed or indoor process unit D, channel E, emission and leakage control F;

[0155] In equation (21), the special process hazard factor F2 has 12 items, including: toxic substances A, negative pressure operation B, operation in or near the combustion range C, dust explosion D, release pressure E, low temperature F, the amount of flammable and unstable substances G, corrosion and abrasion H, leaking joints and packings I, the use of open flame equipment J, hot oil heat exchange system K, rotating equipment L;

[0156] By quantifying the intrinsic safety of the complex distillation system in F&EI, the variables in F&EI are: the penalty term G in F2; G refers to the amount of flammable and unstable substances in the complex distillation system, which is specifically given by equation (22):

[0157] log(G)≥0.17179+0.42988logTB-0.37244log(TB) 2 +0.17712log(TB) 3 -0.029984log(TB) 4 (twenty two)

[0158] In the above equation (22), TB is the heat of the entire distillation process.

[0159] The heat of the entire distillation process is the combustion enthalpy H in the system. C and the total inventory of flammable and unstable substances in the system n The correlation function of is given by equation (23):

[0160] TB=f(inv n ,H c ) (twenty three)

[0161] The total inventory of flammable and unstable substances in the system n is the only variable in the calculation of F&EI, using 30% of the distillation column volume as inventory given by equation (24):

[0162] inv n =30%·vol n (twenty four)

[0163] The volume constraint of the distillation column is given by the plate area Ar n and the spacing H between the plates n The constraint is given by equation (25):

[0164] vol n =Ar n ·H n (25)

[0165] The plate area constraint of the distillation tower is given by equation (26):

[0166]

[0167] The plate area constraint equation and the mass-energy level constraint equation are obtained by calculating the gas phase molar flow rate rf in the nth MES distributor. n,qv By linking them together, the essential safety and economy of the complex distillation system are linked, so as to achieve the purpose of establishing the optimal configuration structure of the complex distillation system.

[0168] Furthermore, step 4 is as follows: establish the proposed NLP model in GAMS and solve it using the CONOPT solver; use the ε-constraint method to perform multi-objective optimization on the NLP model; transform the multi-objective problem into a series of single-objective problems, and other objectives are transferred to the constraints; the optimization scheme will give the following information:

[0169] Optimal configuration structure of complex distillation system; TAC and F&EI of complex distillation system; Heat exchanger heat Q n ; The quantity of flammable and unstable substances in F2 G; The total inventory of flammable and unstable substances inv n ; The molar flow rate and molar composition of gas and liquid phases between each mass and energy level are summarized as shown in the table.

[0170]

[0171] Furthermore, the proposed NLP model was established in GAMS, and the CONOPT solver was used to solve the optimization results. According to the hazard level classification of F&EI, the F&EI value of 60 marks the critical point between the mild and moderate hazard levels. Therefore, the F&EI value of 60 was selected as the optimization result of this implementation case. The rationality of this choice can be further verified by comparing the table data. In terms of total annual cost (TAC), the optimization result is 1520.3 (10 3 $ / yr), only 1489.6 (10 3 $ / yr) is 2.06% higher, but significantly lower than the safest scenario of 3812.0 (10 3$ / yr), and the cost remains within a reasonable range. At the same time, in terms of reboiler heat consumption, the optimization result is 364620.40 kJ / kg, which is only 3.24% higher than the most economical solution of 35318.00 kJ / kg, but much lower than the safest solution of 901879.16 kJ / kg, showing obvious energy economy. In addition, the optimization results also perform well in terms of reserves, G value and F&EI. The reserves of the optimization result are 66.697 kg, which is 42.56% lower than the most economical solution, the G value is reduced from 2.107 to 1.889, and the hazard level is transferred from moderate to mild by reducing F&EI from 63.49 to 60. In contrast, although the F&EI of the safest solution is further reduced to 52.17, its excessive cost and energy consumption make it difficult to apply in practice. In summary, the optimization result of selecting F&EI=60 not only achieves the transition from moderate to mild hazard level and meets the safety requirements, but also maintains high economy in terms of cost and energy consumption. It is the best balance between cost and safety and is suitable for practical engineering applications.

[0172] The above description of the specific exemplary embodiments of the present invention is for illustration and example purposes only, and is not intended to limit the present invention to the specific forms disclosed. Without departing from the spirit and scope of the present invention, those skilled in the art can make various modifications and variations based on the above. The purpose of selecting and describing the exemplary embodiments is to illustrate the basic principles of the present invention and its application, so that those skilled in the art can understand and use the different embodiments and optional improvements of the present invention. The scope of protection of the present invention shall be subject to the attached claims and their equivalents.

Claims

1. An intrinsically safe optimization design method for a complex distillation system based on mass-energy level, characterized in that: The following steps are involved: Step 1: Constrain the mass-energy level MES through the MESH equation; The state space superstructure is constructed by coupling between the distribution network DN and the cascade operation operator SCPO; The MESH equations are material balance, gas-liquid phase balance, additive balance, and energy balance; The mass-energy level MES is the basic building block in a complex distillation system, and the mass-energy level includes material separation, material transfer, and heat exchange; The distribution network DN is responsible for the distribution of materials and heat and is a key component in a complex distillation system. The feed FEED enters from the left side of the distribution network DN and is distributed to each mass-energy level and the mixer before the product stream through the stream distributor as needed; The cascade operation operator SCPO includes all mass-energy level units in the distillation system, wherein any two mass-energy levels are connected by distributors and mixers arranged on the distribution network DN; The state space superstructure is formed by the coupling between the distribution network DN and the cascade operation operator SCPO; in the state space superstructure, all mass and energy levels are connected through the distribution network DN and the cascade operation operator SCPO, so as to realize the material and heat exchange between different streams; Quantify the economics of complex distillation systems based on state-space superstructures; The economics of complex distillation systems are quantified using the total annualized cost (TAC): The general equation (1) represents the total annualized cost of a complex distillation system, which is the equipment cost ec and operating costs oc sum: TAC=cost ec +cost oc (1) Step 2: Select F&EI as the intrinsic safety index for intrinsic safety optimization and quantify the intrinsic safety of the complex distillation system; General equation (2) shows that F&EI is the product of material factor MF, general process hazard factor F1 and special process hazard factor F2: F&EI=MF·F1·F2 (2) Step 3: Based on the state space superstructure, the economy of the complex distillation system is optimized, and the intrinsic safety index F&EI is used to optimize the intrinsic safety of the complex distillation system. The complex distillation configuration structure is designed. The objective functions are to minimize the total annual cost TAC and minimize F&EI of the complex distillation system. In order to minimize TAC and F&EI, nonlinear programming NLP is used to perform multi-objective optimization on the complex distillation system. According to the actual conditions of the distillation process, the upper and lower limits of the optimization variables are specified to obtain the structure-activity relationship between the intrinsic safety and economy of the complex distillation system, and the optimal configuration structure of the system is obtained. The NLP model formula is as follows: Objective function: obj1 = min TAC; obj2 = min F&EI; In the above general equation, obj1 and obj2 are the objective functions; Step 4: Use GAMS solver to solve the NLP model, and obtain the most economical and safest complex distillation configuration structures respectively; obtain the value range of F&EI from the most economical configuration structure and the safest configuration structure; add constraints on the value of F&EI to the NLP model, and then solve it in GAMS to finally obtain a series of Pareto solutions; if no solution that meets the constraints is obtained, return to step 3 to relax the constraint range; Step 5: Select a qualified solution from the Pareto solution set as the optimal feasible solution for the implementation case, wherein the optimal feasible solution meets both the industrial economic feasibility and the intrinsic safety feasibility of the complex distillation system; the intrinsic safety feasibility is achieved through F&EI reduction, which means that in the process of solving the problem, the complex distillation system in the medium or high risk range is gradually reduced to the low risk range.

2. The intrinsically safe optimization design method for a complex distillation system based on mass-energy level according to claim 1, characterized in that: The basic building unit distillation tower plate in the complex distillation system serves as a mass-energy level, and each distillation tower plate serves as a separate heat transfer unit and mass transfer unit. Due to the complex gas-liquid phase flow interaction between the distillation tower plate units, the distillation tower plates presented are not connected sequentially.

3. The intrinsically safe optimization design method for complex distillation systems based on mass-energy levels according to claim 1, characterized in that: The most economical and safest complex distillation configuration was constructed based on the following assumptions and simplifications: 1) The separation process only uses distillation technology; 2) Only steady-state operation is considered during the separation process; 3) Since the Antoine equation is used for phase equilibrium calculation, the system only considers the separation of ideal and near-ideal mixtures; 4) The system is a mixture system composed of multiple components, which has azeotropic properties and will be processed into N different products; 5) The system is fed at the bubble point; 6) To optimize the inherent safety and total annual cost of the system to obtain an optimal distillation configuration structure while meeting specific product requirements.

4. The intrinsically safe optimization design method for complex distillation systems based on mass-energy levels according to claim 1, characterized in that: The equipment cost cost of the complex distillation system in the objective function obj1 ec and operating costs oc The specific expression is equations (3) to (4): cost ec =f(NT,D n ,Ar cod,n ,Ar reb,n ) (3) cost oc =C w ∑Q cod,n s n +C h ∑Q reb,n s n (4) In equation (3), cost ec It includes the number of plates NT and the diameter of the plate D n , Reboiler area Ar cod,n and condenser area Ar reb,n The correlation function of The operating cost in equation (4) is oc Considering the heat of the reboiler and condenser, C w and C h Respectively represent the unit price of cold utility engineering and the unit price of hot utility engineering, σ n is a binary variable describing the existence of heat exchanger n, Q cod,n and Q reb,n They are the heat of the condenser and the heat of the reboiler respectively.

5. The intrinsically safe optimization design method for complex distillation systems based on mass-energy levels according to claim 1 or 2, characterized in that: The physical and chemical parameters of the complex distillation system are: pressure P, bubble point temperature T c , constant pressure specific heat capacity C P ; The kinetic parameters of complex distillation systems are: Antoine constant A c , ambient temperature T0, molar flow rate, vaporization enthalpy h; The mass-energy level unit is constrained by MESH equations; the constraint equations are MESH equations, which are material balance constraints, gas-liquid phase equilibrium constraints, additive equilibrium constraints, and energy balance constraints; The overall material balance constraint of the complex distillation system is given by equation (5): The sum of the total feed molar flow rates of a complex distillation system is equal to the sum of the total product molar flow rates; k is the molar flow rate of feed k, pro s is the molar flow rate of product stream s, FEED is the feed distributor of the distribution network DN, DNMIX U It is a mixer for DN upper boundary products; The mass-energy level material balance constraints are given by equations (6) to (12): Equation (6) is the mass balance constraint of the feed distributor on the left side of DN. The molar flow rate of feed k is distributed to each mass-energy level inlet mixer and each product pre-mixer; k,j is the flow rate from feed distributor k to mixer j, fedp k,j is the flow rate from feed distributor k to product mixer s, DNMIX R It is the DN right boundary mass level import mixer; Equation (7) is the material balance constraint for the product mixer components on the upper side of DN. The product stream can come directly from the feed or from the output of a certain mass-energy level; mc s,c is the molar composition of component c in product mixer s, rf i,s is the molar flow rate from mass-energy level distributor i to product stream s, fedp k,s is the molar flow rate from feed distributor k to product mixer s, mc k,c is the molar composition of component c in feed distributor k, DNMIX L It is the DN lower boundary distributor; Equation (8) is the material balance constraint for the pre-mixer, fs i is the total molar flow rate of stream i leaving the mass-energy level distributor, rf i,s is the molar flow rate from the mass-energy level distributor i to the product mixer s; Equation (9) is the material balance constraint for the distributor on the lower side of DN, fs i is the total molar flow rate of stream i leaving the mass-energy level distributor; Equation (10) is the material balance constraint of the mass-energy level distributor, fs n,q is the molar flow rate of phase q leaving MES stream n, mc n,q,c is the molar composition of stream component c in phase q in MES distributor n, fs n,i,q is the molar flow rate of stream component c of phase q from MES n to mass-energy level distributor i, mc n,i,q,c is the molar composition of stream component c in phase q from MES n to mass-energy level distributor i, rf n,n’,q is the molar flow rate of phase q from MES number n to MES number n', where n, n' are the numbers of the MES; Equation (11) is the material balance constraint of the mixer on the mass-energy level, rf n,q is the molar flow rate of phase q in MES distributor n, rf n’,n,q is the molar flow rate of phase q from MES n' to MES n; Equation (12) is the total material balance constraint for mass-energy level; The gas-liquid phase equilibrium constraints of the complex distillation system are given by equations (13) to (14): Equation (13) is the gas-liquid phase equilibrium constraint of the complex distillation system, where mc n,qv,c is the molar composition of gaseous stream component c in MES distributor n, mc n,ql,c is the molar composition of liquid stream component c of MES distributor n, K n is the equilibrium constant of MES n; Equation (14) is the Antoine constraint, P is the total pressure of the complex distillation system, T n is the temperature of MES n, A c , B c and C c are the Antoine constants of component c respectively; The additive equilibrium constraint of the complex distillation system is given by equation (15): Equation (15) is an additive constraint, the sum of each component is equal to 1, mc q,c is the molar composition of component c in phase q; The total heat balance constraint of the complex distillation system is given by equation (16): hfed k is the specific enthalpy of feed stream k, hpro s is the specific enthalpy of product stream s; The total heat balance constraint of mass-energy level is given by equations (17) to (19): Equation (17) is the energy balance constraint for the mass-energy level pre-mixer, hfs j is the specific enthalpy of stream j in the pre-mixer of MES n = j, hfed k is the specific enthalpy of feed stream k; Equation (18) is the energy balance constraint for the product mixer components on the upper side of DN, hrf n,q is the specific enthalpy of phase q in MES n, hrf n’,q is the specific enthalpy of phase q in MES n'; Equation (19) is the total energy balance constraint for mass-energy level, hfs n,q is the specific enthalpy of phase q in MES n; The combined effect of the above constraint equations ensures the rationality and balance of the complex interactions between mass-energy levels.

6. The intrinsically safe optimization design method for complex distillation systems based on mass-energy levels according to claim 5, characterized in that: The general process risk factor F1 and the special process risk factor F2 of the complex distillation system in the objective function obj2 are specifically expressed as equations (20) to (21): In equation (20), there are 6 general risk factors, including: exothermic reaction A, endothermic reaction B, material handling and transportation C, closed or indoor process unit D, channel E, emission and leakage control F; In equation (21), the special process hazard factor F2 has 12 items, including: toxic substances A, negative pressure operation B, operation in or near the combustion range C, dust explosion D, release pressure E, low temperature F, the amount of flammable and unstable substances G, corrosion and abrasion H, leaking joints and packings I, the use of open flame equipment J, hot oil heat exchange system K, rotating equipment L; By quantifying the intrinsic safety of the complex distillation system in F&EI, the variables in F&EI are: the penalty term G in F2; G refers to the amount of flammable and unstable substances in the complex distillation system, which is specifically given by equation (22): log(G)≥0.17179+0.42988logTB-0.37244log(TB) 2 +0.17712log(TB) 3 -0.029984log(TB) 4 (22) In the above equation (22), TB is the heat of the entire distillation process.

7. The intrinsically safe optimization design method for complex distillation systems based on mass-energy levels according to claim 6, characterized in that: The heat of the entire distillation process is the combustion enthalpy H C and the total inventory of flammable and unstable substances in the system n The correlation function of is given by equation (23): TB=f(inv n ,H c ) (23)。 8. The intrinsically safe optimization design method for complex distillation systems based on mass-energy levels according to claim 7, characterized in that: The total inventory of flammable and unstable substances in the system n is the only variable in the calculation of F&EI, using 30% of the distillation column volume as inventory given by equation (24): inv n =30%·vol n (24) The volume constraint of the distillation column is given by the plate area Ar n and the spacing H between the plates n The constraint is given by equation (25): vol n =Ar n ·H n (25) The plate area constraint of the distillation tower is given by equation (26):

9. The intrinsically safe optimization design method for complex distillation systems based on mass-energy level according to claim 4 or 8, wherein the plate area constraint equation and the mass-energy level constraint equation are calculated by calculating the gas phase molar flow rate rf in the nth MES distributor. n,qv By linking them together, the essential safety and economy of the complex distillation system are linked, so as to achieve the purpose of establishing the optimal configuration structure of the complex distillation system.

10. The intrinsically safe optimization design method for complex distillation systems based on mass-energy levels according to claim 1, characterized in that: The step 4 is specifically as follows: establishing the proposed NLP model in GAMS and solving it using the CONOPT solver; using the ε-constraint method to perform multi-objective optimization on the NLP model; converting the multi-objective problem into a series of single-objective problems, and other objectives are transferred to the constraints; the optimization scheme gives the following information: the optimized configuration structure of the complex distillation system; the TAC and F&EI of the complex distillation system; Heat exchanger heat Q n ; The quantity of flammable and unstable substances in F2 G; The total inventory of flammable and unstable substances inv n ; The molar flow rate and molar composition of gas and liquid phases between each mass and energy level.

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