An intrinsically safe optimization design method for complex distillation systems based on mass-energy levels

Abstract

The present invention belongs to the field of computer-aided optimization design technology for complex distillation systems and chemical process safety, and discloses a method for intrinsically safe optimization design of complex distillation systems based on mass-energy levels. By constructing a nonlinear programming model based on mass-energy levels, it aims to design an optimal configuration structure that combines safety and economy for a complex distillation system. The optimization goal is to determine the optimal configuration structure by balancing the total annualized cost and intrinsic safety of the complex distillation system. This method uses the mass-energy level as the smallest unit of the complex distillation system, thereby expanding the scope of understanding space and effectively avoiding the risk of misjudging a suboptimal configuration structure as the optimal one. In addition, the present invention associates the key variables in the simplified distillation model with the fire and explosion index, taking into account both intrinsic safety and total annualized cost. The present invention provides a reliable and efficient solution for the intrinsically safe optimization design of complex distillation systems, which is suitable for multi-objective optimization needs in actual 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, primarily used for the separation and purification of hazardous chemicals. Its energy consumption accounts for over 50% of total production energy consumption. As an advanced distillation technology, complex distillation offers significant advantages over traditional distillation in terms of separation efficiency, energy consumption, flexibility, product quality, process safety, adaptability to new technologies, and economic efficiency. 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, this technology exhibits greater flexibility and can effectively respond to changes in different feed components to meet market demand for high-purity products.

[0003] Despite the significant advantages of complex distillation technology for multicomponent separation, its practical application faces challenges such as high design and operation complexity, high initial investment, and stringent requirements for equipment and processes. These factors significantly increase the difficulty of process management and optimization. To address these issues, Zou et al. introduced the concepts of supertowers and mass-energy levels in "Study on a Comprehensive Method for Multicomponent Thermally Coupled Distillation Separation Systems," fundamentally simplifying the separation process of multicomponent non-azeotropic mixtures, reducing the complexity and cost of the separation system, and improving separation efficiency. To further optimize process management, Zhu et al. optimized the extractive distillation process using a parallel genetic algorithm in "Research Progress and Prospects of Intrinsic Safety Assessment Methods for Chemical Processes," maximizing net revenue while significantly reducing total annual costs. Meanwhile, Sun et al. proposed an extractive distillation scheme combining heat integration with a vapor 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," further simplifying the process flow and significantly reducing costs. These research results significantly reduce the difficulty of managing and optimizing complex distillation, providing effective solutions for economical and efficient separation processes.

[0004] However, as the petrochemical industry pursues low-energy, low-cost, and highly safe separation technologies, process safety remains a core concern. Conceptual design often prioritizes economic and environmental factors, neglecting safety considerations. This leads to the need for additional control and protection measures in the safety design of complex distillation systems, increasing system complexity. These safety devices often only mitigate the consequences of accidents, failing to effectively prevent them. To this end, Vázquez et al., in "Multi-objective Early Design of Complex Distillation Sequences Considering Economic and Inherent Safety Criteria," combined the Deb algorithm with principal component analysis to directly incorporate intrinsic safety into distillation column design, achieving an economically feasible and inherently safe optimized design. Medina et al., in "A new index for chemical process design considering risk analysis and controllability symposium," emphasized the importance of integrating safety and controllability into the early stages of process design. Using this new index, they achieved a safer, intrinsically safe process, demonstrating a significant shift in integrating safety into process design.

[0005] Furthermore, Zhu et al., in "Development of novel hybrid pre-separation / extractive reactive distillation processes for the separation of methanol / methylacetate / ethyl acetate," integrated intrinsic safety, environmental, and economic factors to optimize the distillation concept design for propylene glycol monomethyl ether acetate production. Ye et al., in "An inherently safer development approach for thermally coupled distillation sequences: Application inhazardous chemical separation," developed a comprehensive framework for intrinsic safety in thermally coupled distillation sequences. This framework effectively integrates and optimizes safety considerations, utilizing a state-task network (STN) and the Dow Fire and Explosion Index (F&EI) to assess the intrinsic safety of each column configuration. However, existing intrinsic safety optimization designs for complex distillation systems primarily focus on distillation column sections and sequences, which results in an incomplete optimization solution space, potentially leading to suboptimal configurations and increased risks and costs.

[0006] To address the inherent safety optimization design of complex distillation systems, this study considers mass-energy levels as the fundamental units of complex distillation systems. The goal is to explore the interconnections between gas-liquid phase streams at each stage, thereby designing a complex distillation system that considers both intrinsic safety and economic factors. Within this context, implementing inherent safety optimization design methods in complex distillation systems still faces two major challenges. First, existing studies often consider tower sections or distillation sequences as system units, resulting in an incomplete optimization solution space and potentially suboptimal configurations. This increases safety feature redundancy in actual designs, further increasing risk and cost budgets. Second, coupling system safety and economics within a simplified distillation model is a significant challenge. Selecting an appropriate intrinsic safety index to quantify the safety of complex distillation systems and analyzing the resulting optimized configurations present significant challenges. Therefore, identifying the research units for complex distillation and effectively coupling safety and economic factors remain pressing challenges. 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 complex distillation systems based on mass-energy levels. A nonlinear programming (NLP) model is used to optimize the total annualized cost (TAC) and intrinsic safety index (ISI) of the complex distillation system. The Pareto solution in the solution process is obtained through the ε-constraint method, so as to obtain the structure-performance relationship between the intrinsic safety and economy of the complex distillation system and obtain the optimal configuration structure of the system.

[0008] The technical solution of the present invention is as follows: a method for optimizing the intrinsic safety of a complex distillation system based on mass-energy levels, comprising the following steps:

[0009] Step 1: Constrain the mass-energy level MES through the MESH equation; construct the state space superstructure by coupling 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 complex distillation systems. 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 of the complex distillation system. The feed FEED enters from the left side of the distribution network DN and is distributed to each mass and 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 operator SCPO. In this state-space superstructure, all mass and energy levels are connected through the distribution network DN and the cascade operator SCPO, thereby enabling material and heat exchange between different streams. Based on the state-space superstructure, the economic performance of complex distillation systems is quantified.

[0015] The economics of complex distillation systems are quantified using the total annualized cost (TAC): The general equation (1) expresses 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 the F&EI as the intrinsic safety index for intrinsic safety optimization and quantify the intrinsic safety of complex distillation systems. The F&EI is one of the most widely used safety indices in the process industry. Its widespread application is primarily due to its ability to effectively estimate potential losses from fire and explosions, providing a reliable basis for implementing control measures. Furthermore, the F&EI is not only a quantitative risk analysis method but also plays a crucial role in unit-level hazard identification, supporting inherently safer designs. Therefore, this study selected the F&EI as the intrinsic safety index for safety optimization of complex distillation systems.

[0018] The general equation (2) states 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 economic efficiency of the complex distillation system is optimized. The intrinsic safety index (F&EI) is used to optimize the intrinsic safety of the complex distillation system, and a complex distillation configuration structure is designed. The objective functions are to minimize the total annual cost (TAC) and the F&EI of the complex distillation system. To minimize TAC and F&EI, nonlinear programming (NLP) is used to perform multi-objective optimization of the complex distillation system. The upper and lower bounds of the optimization variables are set according to the actual conditions of the distillation process. The structure-performance relationship between the intrinsic safety and economic efficiency of the complex distillation system is obtained, 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 objective functions;

[0023] Step 4: Use the GAMS solver to solve the NLP model and obtain the most economical and safest complex distillation configurations. From the most economical and safest configurations, obtain the range of F&EI values. Add F&EI value constraints to the NLP model and solve it in GAMS to obtain a series of Pareto solutions. If no solution that meets the constraints is obtained, return to step 3 to relax the constraints.

[0024] Step 5: Select a qualified solution from the Pareto solution set as the optimal feasible solution for the implementation case. 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. The F&EI reduction means gradually reducing the complex distillation system from a medium or high risk range to a low risk range during the solution process.

[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 is 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 with 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 and obtain an optimal distillation configuration structure under the premise of 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 project and the unit price of hot utility project, 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 physicochemical 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 flows of a complex distillation system is equal to the sum of the total product molar flows; k is the molar flow rate of feed k, pro s is the molar flow rate of the 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 inlet 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 the mass-energy level distributor i to the 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 of 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 the stream component c of 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 equilibrium constraints of a complex distillation system are given by equations (13) to (14):

[0053]

[0054] Equation (13) is the gas-liquid 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 number 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;

[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 the product stream s;

[0062] The total heat balance constraints of mass-energy levels are 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 the 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 levels, hfs n,q is the specific enthalpy of phase q in MES number 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 six 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 packing I, use of open flame equipment J, hot oil heat exchange system K, rotating equipment L;

[0072] The intrinsic safety of the complex distillation system is optimized by quantifying the F&EI. The variables in the F&EI are: the penalty term G in F2; G refers to the amount of flammable and unstable substances in the complex distillation system, 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 HC 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 inv 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 determined 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 tray area constraint of the distillation column is given by equation (26):

[0082]

[0083] The plate area constraint equation and the mass energy level constraint equation are calculated by 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; performing multi-objective optimization on the NLP model using the ε-constraint method; transforming the multi-objective problem into a series of single-objective problems, with other objectives being transferred to the constraints; and providing the following information in the optimization solution: the optimized configuration structure of the complex distillation system; the TAC and F&EI of the complex distillation system; the heat exchanger heat Q n ; The quantity G of flammable and unstable substances in F2; 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 conducts intrinsically safe design of complex distillation systems based on mass-energy levels, which faces two challenges in practical application. First, the intrinsically safe optimization design of existing complex distillation systems usually uses tower sections or distillation sequences as research units. This approach 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 costs. Therefore, the rational determination of the research units of complex distillation systems is the key to ensuring the quality of optimization results.

[0087] Another major challenge is effectively coupling the system's inherent safety and economic efficiency within the simplified distillation model. Selecting appropriate intrinsic safety indicators to quantify the safety of complex distillation systems and, based on these indicators, scientifically analyzing and selecting the optimal configuration structure is crucial for achieving system design goals.

[0088] To address these challenges, this paper introduces a state-space superstructure based on mass-energy levels, further subdividing the research unit of complex distillation systems into a single, fundamental unit. Each tray not only independently performs mass and heat transfer processes but also provides input and output functions, and the interconnection between trays is not restricted to a fixed sequence. This approach significantly expands the scope of the understanding space and effectively enhances the flexibility and completeness of the optimization results.

[0089] Furthermore, the present invention quantifies the intrinsic safety of the system through the Fire and Explosion Index (F&EI), assesses safety using system reserves, and correlates the molar flow rates of the gas and liquid phases with economic parameters, enabling the system to achieve an organic combination of safety and economic efficiency in a multi-objective optimization process. Finally, the present invention proposes an optimization design method that combines the mass-energy level state space superstructure and intrinsic safety indicators for the intrinsic safety design of complex distillation systems, significantly expanding the safety range of system design and providing a highly 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 A flow chart of the intrinsic safety design method for a complex distillation system based on mass-energy levels according to 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] Based on this idea, an engineering example was used: the process of distillation optimization design of the 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] This method was applied to a practical engineering example: the separation of a ternary azeotropic aromatic mixture of BTX. Previous studies have established a simple distillation model, with known physicochemical parameters and operating conditions. The following provides a detailed implementation and specific operating procedure for this method, but the scope of the present invention is not limited to these examples.

[0097] See Figure 2 The present invention specifically discloses an intrinsically safe optimization design framework for a complex distillation system based on mass-energy level, comprising the following steps:

[0098] Step 1: Constrain the mass-energy level MES through the MESH equation; construct the state space superstructure by coupling the distribution 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. The economic performance of the complex distillation system is quantified using the total annual cost TAC: The general equation (1) represents the total annual 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 to perform intrinsic safety optimization and quantify the intrinsic safety of the complex distillation system; 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:

[0101] F&EI=MF·F1·F2 (2)

[0102] Step 3: Based on the state-space superstructure, the economic efficiency of the complex distillation system is optimized. The intrinsic safety index (F&EI) is used to optimize the intrinsic safety of the complex distillation system, and a complex distillation configuration structure is designed. The objective functions are to minimize the total annual cost (TAC) and the F&EI of the complex distillation system. To minimize TAC and F&EI, nonlinear programming (NLP) is used to perform multi-objective optimization of the complex distillation system. The upper and lower bounds of the optimization variables are set according to the actual conditions of the distillation process. The structure-performance relationship between the intrinsic safety and economic efficiency of the complex distillation system is obtained, 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 the GAMS solver to solve the NLP model and obtain the most economical and safest complex distillation configurations. From the most economical and safest configurations, obtain the range of F&EI values. Add F&EI value constraints to the NLP model and solve it in GAMS to obtain a series of Pareto solutions. If no solution that meets the constraints is obtained, return to step 3 to relax the constraints.

[0105] Step 5: Select a solution from the Pareto solution set that meets the requirements as the optimal feasible solution for the implementation case. This optimal feasible solution meets both the industrial economic feasibility and the intrinsic safety feasibility of the complex distillation system. Intrinsic safety feasibility is achieved through F&EI order reduction, which involves gradually reducing the complex distillation system from a medium or high risk range to a low risk range during the solution process. This is 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 phase 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 with 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 intrinsic safety and total annualized cost of the system while meeting specific product requirements, and to obtain an optimal distillation configuration structure.

[0114] Furthermore, the intrinsically safe optimization design method for a complex distillation system based on mass-energy level 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 project and the unit price of hot utility project, 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 flows of a complex distillation system is equal to the sum of the total product molar flows; k is the molar flow rate of feed k, pro s is the molar flow rate of the 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 inlet 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 the mass-energy level distributor i to the 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 of 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 the stream component c of 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 equilibrium constraints of a complex distillation system are given by equations (13) to (14):

[0136]

[0137] Equation (13) is the gas-liquid 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 number 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;

[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 the product stream s;

[0145] The total heat balance constraints of mass-energy levels are 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 the 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 levels, hfs n,q is the specific enthalpy of phase q in MES number 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 six 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 packing I, use of open flame equipment J, hot oil heat exchange system K, rotating equipment L;

[0156] The intrinsic safety of the complex distillation system is optimized by quantifying the F&EI. The variables in the F&EI are: the penalty term G in F2; G refers to the amount of flammable and unstable substances in the complex distillation system, 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 C and the total inventory of flammable and unstable substances in the system n The correlation function 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 inv 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 determined 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 tray area constraint of the distillation column is given by equation (26):

[0166]

[0167] The plate area constraint equation and the mass energy level constraint equation are calculated by 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: the proposed NLP model is established in GAMS and solved using the CONOPT solver; the NLP model is optimized for multiple objectives using the ε-constraint method; the multi-objective problem is transformed into a series of single-objective problems, and the other objectives are transferred to the constraints; the optimization solution will give the following information:

[0169] Optimal configuration structure of complex distillation systems; TAC and F&EI of complex distillation systems; heat exchanger heat Q n ; The quantity G of flammable and unstable substances in F2; The total inventory of flammable and unstable substances inv n The molar flow rates and molar compositions of the gas and liquid phases between each mass and energy level are summarized 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 F&EI hazard level classification, 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), keeping costs within a relatively reasonable range. Meanwhile, the optimized reboiler heat consumption reached 364,620.40 kJ / kg, a mere 3.24% increase compared to the most economical solution's 35,318.00 kJ / kg, but significantly lower than the safest solution's 901,879.16 kJ / kg, demonstrating significant energy economy. Furthermore, the optimized results excelled in terms of reserves, G value, and F&EI. The optimized reserves reached 66.697 kg, a 42.56% reduction compared to the most economical solution, while the G value decreased from 2.107 to 1.889. The hazard level shifted from moderate to mild by reducing the F&EI from 63.49 to 60. In contrast, although the F&EI of the safest solution was further reduced to 52.17, its excessive cost and energy consumption made it difficult to implement 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 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 may 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 utilize different embodiments and optional improvements of the present invention. The scope of protection of the present invention shall be based on the appended claims and their equivalents.

Claims

1. A method for optimizing the intrinsic safety of 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 complex distillation systems. 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 of the complex distillation system. The feed FEED enters from the left side of the distribution network DN and is distributed to each mass and 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 this state space superstructure, all mass and energy levels are connected through the distribution network DN and the cascade operation operator SCPO, thereby realizing 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) expresses 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 to perform intrinsic safety optimization and quantify the intrinsic safety of the complex distillation system; 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: F&EI=MF·F1·F2 (2) Step 3: Based on the state-space superstructure, the economic efficiency of the complex distillation system is optimized. The intrinsic safety index (F&EI) is used to optimize the intrinsic safety of the complex distillation system, and a complex distillation configuration structure is designed. The objective functions are to minimize the total annual cost (TAC) and the F&EI of the complex distillation system. To minimize TAC and F&EI, nonlinear programming (NLP) is used to perform multi-objective optimization of the complex distillation system. The upper and lower bounds of the optimization variables are set according to the actual conditions of the distillation process. The structure-performance relationship between the intrinsic safety and economic efficiency of the complex distillation system is obtained, 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 objective functions; Step 4: Use the GAMS solver to solve the NLP model and obtain the most economical and safest complex distillation configurations. From the most economical and safest configurations, obtain the range of F&EI values. Add F&EI value constraints to the NLP model and solve it in GAMS to obtain a series of Pareto solutions. If no solution that meets the constraints is obtained, return to step 3 to relax the constraints. Step 5: Select a qualified solution from the Pareto solution set as the optimal feasible solution for the implementation case. 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. The F&EI reduction means gradually reducing the complex distillation system from a medium or high risk range to a low risk range during the solution process.

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 a complex distillation system based on mass-energy level according to claim 1, characterized in that: The most economical and safest complex distillation configuration is 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 with 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 and obtain an optimal distillation configuration structure under the premise of meeting specific product requirements.

4. The intrinsically safe optimization design method for a complex distillation system based on mass-energy level 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 project and the unit price of hot utility project, 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 a complex distillation system based on mass-energy level according to claim 1 or 2, characterized in that: The physicochemical 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 flows of a complex distillation system is equal to the sum of the total product molar flows; k is the molar flow rate of feed k, pro s is the molar flow rate of the 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 inlet 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 the mass-energy level distributor i to the 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 of 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 the stream component c of 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 equilibrium constraints of a complex distillation system are given by equations (13) to (14): Equation (13) is the gas-liquid 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 number 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; 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 the product stream s; The total heat balance constraints of mass-energy levels are 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 the 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 levels, hfs n,q is the specific enthalpy of phase q in MES number 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 a complex distillation system based on mass-energy level 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 six 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 packing I, use of open flame equipment J, hot oil heat exchange system K, rotating equipment L; The intrinsic safety of the complex distillation system is optimized by quantifying the F&EI. The variables in the F&EI are: the penalty term G in F2; G refers to the amount of flammable and unstable substances in the complex distillation system, 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 a complex distillation system based on mass-energy level 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 is given by equation (23): TB=f(inv n ,H c ) (23)。 8. The intrinsically safe optimization design method for a complex distillation system based on mass-energy level according to claim 7, characterized in that: The total inventory of flammable and unstable substances in the system inv 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 determined 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 tray area constraint of the distillation column is given by equation (26):

9. The intrinsically safe optimization design method for a complex distillation system 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 a complex distillation system based on mass-energy level according to claim 1, characterized in that: Specifically, step 4 includes establishing the proposed NLP model in GAMS and solving it using the CONOPT solver; performing multi-objective optimization on the NLP model using the ε-constraint method; transforming the multi-objective problem into a series of single-objective problems, with other objectives being transferred to the constraints; and providing the following information in the optimization solution: 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 G of flammable and unstable substances in F2; 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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