Geothermal power generation system operation optimization method considering time-varying characteristics of boundary parameters

Abstract

This invention relates to an operation optimization method for geothermal power generation systems that considers the time-varying characteristics of boundary parameters, aiming to achieve efficient and low-cost sustainable utilization of geothermal energy, applicable to the energy utilization field. Based on collected geothermal resource data, local climate data, and hydrological condition data, this invention establishes the time-varying characteristics of relevant boundary parameters for system operation; based on these characteristics, equipment selection is performed; and according to the design and operating characteristics of key system equipment among the selected equipment, the response relationship of key equipment parameters to the time-varying boundary parameters is constructed; based on this response relationship, a thermodynamic optimization objective function is established with the goal of maximizing net power output; the power generation over the entire life cycle of the system is calculated by combining the optimization results of the thermodynamic optimization objective function; when the optimization results of the economic optimization objective function fail to meet the requirements, the equipment selection is readjusted, and through iteration, the global optimization parameters are obtained recursively.

Description

Technical Field

[0001] This invention relates to an operation optimization method for geothermal power generation systems that considers the time-varying characteristics of boundary parameters. It is applicable to the field of energy utilization. Background Technology

[0002] Compared to solar and wind power, geothermal energy boasts advantages such as stable temperature, immunity to seasonal weather conditions, and high utilization efficiency. Its stable power generation contributes to the safe and stable operation of the power grid, thus attracting widespread attention both domestically and internationally in recent years. Although my country possesses abundant geothermal resources, approximately 70% are low- to medium-temperature hydrothermal resources. The thermal efficiency of low- to medium-temperature geothermal power generation remains relatively low, resulting in high investment costs per unit of power. Furthermore, the extracted geothermal water requires reinjection, further increasing operation and maintenance costs.

[0003] Currently, the optimization of geothermal power generation system parameters mainly considers load requirements, and adjusting parameters often results in the unit operating outside its optimal condition. Therefore, there is an urgent need to develop a multi-dimensional, multi-objective optimization method that can consider the time-varying characteristics of boundary parameters, and whose optimization scheme can take into account both equipment operating characteristics and environmental constraints, while also considering economic efficiency.

[0004] In actual operation, geothermal power generating units are affected by the time-varying characteristics of environmental conditions and grid dispatch, causing changes in the unit's operating status and thermodynamic characteristics. This directly or indirectly affects its power generation, plant power consumption rate, geothermal water flow, and so on, thus impacting the unit's operational economy. Furthermore, emissions of gases (H2S, CO2, SO2, etc.), geothermal water discharge, and thermal emissions generated during geothermal water extraction are subject to environmental protection restrictions. Therefore, it is essential to conduct refined thermodynamic and economic performance optimization of geothermal power generating units, comprehensively considering equipment operating characteristics, real-time impacts of external environmental boundaries, and environmental emission incentives and penalties. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for optimizing the operation of a geothermal power generation system that considers the time-varying characteristics of boundary parameters, so as to achieve efficient and low-cost sustainable utilization of geothermal energy.

[0006] The technical solution adopted in this invention is: a method for optimizing the operation of a geothermal power generation system considering the time-varying characteristics of boundary parameters, characterized in that:

[0007] Based on the collected geothermal resource data, local climate data, and hydrological condition data, the time-varying characteristics of the relevant boundary parameters for system operation are established.

[0008] Equipment selection is based on the time-varying characteristics of system operation-related boundary parameters. Based on the design and operation characteristics of the key equipment in the selected equipment, the response relationship of the key equipment parameters with the time-varying boundary parameters is constructed.

[0009] Based on the time-varying response relationship of key equipment parameters with boundary parameters, a thermodynamic optimization objective function is established with the goal of maximizing net output work.

[0010] The power generation of the system throughout its entire life cycle is calculated by combining the optimization results of the thermodynamic optimization objective function. The power generation cost of the system throughout its entire life cycle is calculated based on the power generation of the system throughout its entire life cycle and the total investment and operating cost. An economic optimization objective function with the goal of minimizing the power generation cost is then established.

[0011] When the optimization result of the economic optimization objective function fails to meet the requirements, the equipment selection is readjusted, and through iteration, the global optimization parameters are obtained step by step recursively.

[0012] The boundary parameters include power generation load demand, environmental parameters, heat source parameters, and reinjection parameters.

[0013] The time-varying response relationship of the key equipment parameters with respect to boundary parameters includes:

[0014] A set of functions relating heat transfer load to time-varying boundary parameters

[0015] h(x)=[h1(x1),h2(x2),h3(x3)…h n (x n )];

[0016] A set of functions relating power generation to time-varying boundary parameters

[0017] w(x)=[w1(x1),w2(x2),w3(x3)…w n (x n )];

[0018] A set of functions relating the power of energy-consuming equipment to time-varying boundary parameters.

[0019] g(x)=[g1(x1),g2(x2),g3(x3)…g n (x n )];

[0020] Where, x i To influence the time-varying parameters of the equipment, n is the number of boundary time-varying parameters involved in the equipment.

[0021] The thermodynamic optimization objective function is:

[0022]

[0023] W represents the power generation function; G represents the power consumption function; τ i This refers to the corresponding time-varying period.

[0024] The economic optimization objective function is:

[0025]

[0026] Among them, C F Invest in all equipment; F,i The unit price of a certain piece of equipment; f i The correlation function for this equipment should consider economic factors such as the number of devices (including spares), depreciation, and loan interest rates; C E Energy cost; c E,i The unit cost of a certain amount of energy consumed; m i τ is a correlation function for mass flow rate or corresponding quantity, considering economic factors such as price fluctuations; i For the corresponding time-varying time period; C O For operating costs, C M To maintain costs; C A Income includes environmental incentives and subsidies for carbon emissions and other purposes; W represents the electricity generation function.

[0027] The constraints of the thermodynamic optimization objective function include:

[0028] The covariant relationship of parameters between key system equipment and processes is used as a constraint condition.

[0029] The operating parameters constrain the boundary conditions based on the characteristics of the equipment.

[0030] The constraints of the economic optimization objective function include:

[0031] The covariant relationship of parameters between key system equipment and processes is used as a constraint condition.

[0032] Boundary conditions constrained by operating parameters determined based on equipment characteristics;

[0033] Safety constraints;

[0034] Emission constraints.

[0035] The beneficial effects of this invention are as follows: This invention establishes the time-varying characteristics of boundary parameters based on the collected data, and selects equipment based on the time-varying characteristics of boundary parameters. Subsequently, it constructs thermodynamic optimization objective functions and economic optimization objective functions based on the time-varying response relationship of key equipment parameters with boundary parameters. Through the thermodynamic optimization objective functions and economic optimization objective functions, it calculates and obtains the global operation optimization parameters, thereby achieving refined thermodynamic and economic performance optimization that comprehensively considers equipment operating characteristics, real-time influence of external environmental boundaries, and environmental emission incentives and penalties.

[0036] This invention employs a two-layer nonlinear optimization solution model, dividing the problem into two levels: thermodynamic optimization and economic optimization. Thermodynamic optimization is the upper-level planning solution, while economic optimization is the lower-level planning solution. The optimization information from the upper level is passed to the lower level, and the lower level feeds back the optimization result based on minimizing operating costs to the upper level. The global operating optimization parameters are obtained through step-by-step recursion. Attached Figure Description

[0037] Figure 1 The flowchart is for an example. Detailed Implementation

[0038] This embodiment presents an operation optimization method for a geothermal power generation system considering the time-varying characteristics of boundary parameters, specifically including the following steps:

[0039] S1. Based on the collected geothermal resource data, local climate data, and hydrological condition data, establish the time-varying characteristics of the relevant boundary parameters for system operation.

[0040] Geothermal resource data primarily originates from geological exploration, including geothermal gradient, reservoir lithology, reservoir rock properties, geothermal heat flow, heat recovery cycle, water quality conditions (dissolved salt composition), and geothermal well structural parameters. Based on this data and combined with numerical simulation methods, parameters such as temperature, pressure, and dryness of the wellhead fluid are determined. Different geothermal fluid extraction flow rates affect the geothermal extraction lifespan. For the entire life cycle, a reasonable geothermal water extraction volume is established, and the time-varying characteristics of the wellhead fluid temperature, pressure, and flow rate are established. The changes in these parameters consider short-term fluctuations and time-varying characteristics, used to characterize the dynamic properties of the system, such as the temperature difference of heat exchange equipment, etc. The relationship between the coordinated changes in material flow and energy flow is considered, along with the thermal decay of geothermal resources over a long period. The time-varying characteristics of geothermal fluid parameters during the extraction cycle are mainly used for power generation calculation and prediction, economic evaluation, etc. Based on geothermal water flow rate, pressure, and dissolved salt composition, and considering the precipitation characteristics of dissolved salts, the descaling and maintenance cycle of heat exchange equipment and pipelines is taken into account to determine the reinjection temperature. Based on the reinjection scheme, reinjection well structural parameters, rock structural parameters and physical properties of the reinjection layer, the reinjection pressure is determined, and the pressure fluctuation characteristics of the fluid at the reinjection wellhead, especially the pressure change characteristics at the reinjection wellhead over a long period, are grasped to calculate the energy consumption of the reinjection pump.

[0041] Local climate parameters, mainly including ambient temperature, humidity, and pressure, are collected and relevant data are statistically analyzed. In particular, the hourly characteristics of ambient temperature and pressure are determined to identify the ambient temperature for the design operating conditions of the geothermal power generation system, which is then used for the selection and design of the cold end system.

[0042] Hydrological data is used for the selection and timing of cold-end systems. For example, in areas with low average ambient temperature, air-cooled condensers are preferred, while in areas with high average temperature and abundant water resources, water-cooled condensers are preferred. In plateau areas, where the environmental pressure and average temperature are low, working fluids with low normal boiling points can be selected. Among these factors, the hourly characteristics of environmental parameters affect the operating characteristics of the cold end of the geothermal generator set.

[0043] In this example, the time-varying characteristics of the boundary parameters include the temperature, pressure, and flow rate fluctuations of the geothermal fluid, especially the attenuation characteristics of geothermal fluid temperature and flow rate with increasing mining years; power generation load demand; hourly changes in local ambient temperature and humidity; molten salt composition and scaling characteristics of the geothermal fluid; considering the allowable limits of scaling in pipelines, valves, and heat exchangers and its impact on unit shutdown, the optimal reinjection temperature is selected; and the time-varying characteristics of reinjection pressure and flow rate are established considering the structural parameters of the reinjection well and reinjection layer. Statistical summarization of the time-varying characteristics of the above parameters is performed to determine the upper and lower limits of the parameters, and the hourly characteristic function relationship of the above parameters is established. The parameter construction characterization model is as follows.

[0044] Temperature boundary:

[0045]

[0046] Pressure boundary:

[0047]

[0048] Mass flow rate boundary of fluid:

[0049]

[0050] S2. Select equipment based on the time-varying characteristics of the system's operational boundary parameters. Based on the design and operational characteristics of the key equipment in the selected equipment, construct the response relationship between the key equipment parameters and the time-varying boundary parameters.

[0051] System design and selection:

[0052] Circulation Type: The temperature of the geothermal fluid at the production wellhead and the reinjection temperature are the main bases for selecting the circulation type. For production wellhead temperatures below 140℃, subcritical organic Rankine circulation is preferred; for wellhead temperatures below 120℃, single-pressure evaporation subcritical organic Rankine circulation is preferred. For geothermal sources with large geothermal fluid flow rates and wellhead temperatures above 120℃, dual-pressure evaporation subcritical circulation can be selected. For production wellhead temperatures above 140℃, supercritical organic Rankine circulation, dual-pressure evaporation circulation, and flash circulation can be selected. The choice of circulation type also needs to be matched with the working fluid. For geothermal fluids with wellhead temperatures above 150℃ and dryness fraction above 0.15, a composite circulation of fluid flash evaporation, steam expansion for power generation, and geothermal water driving organic Rankine circulation is preferred. Organic circulation uses regeneration, which can often improve the optimal reinjection temperature. For geothermal sources with a high range of reinjection temperature limitations, regeneration circulation can be preferred. In addition to considering the temperature parameters and energy flow of the geothermal source, the thermodynamic characteristics and economy of the circulation are also the main bases for design, which will be further explained later.

[0053] Working fluid selection: Low-GWP environmentally friendly working fluids, such as hydrocarbon or HFO fluids, are preferred. Based on the slope of the saturated vapor line, working fluids can be categorized as dry or wet. For geothermal wellhead temperatures below 120°C, and preferably subcritical cycles, dry working fluids with a critical temperature below 150°C or wet working fluids with a critical temperature below 120°C can be used. Wet working fluids require a certain degree of superheat to avoid expansion in the gas-liquid two-phase region. For production wellhead temperatures above 140°C, and preferably supercritical cycles, working fluids with a critical temperature below 110°C are preferred. If the reinjection temperature... For applications with high requirements, a dry working fluid with a critical temperature below 110°C should be preferred, and reheating should be used. If there are no restrictions on reinjection temperature, a wet working fluid with a critical temperature below 110°C is preferred. For wellhead temperatures above 140°C, a dual-pressure evaporation organic Rankine cycle is preferred, with a dry working fluid having a critical temperature above 160°C. For wellhead temperatures above 140°C, a single-pressure evaporation subcritical organic Rankine cycle is preferred, with a dry working fluid having a critical temperature below 160°C. For wellhead temperatures above 140°C, a flash evaporation cycle is preferred, with a dry working fluid having a critical temperature above 160°C.

[0054] Cooling method selection: For regions with an average annual temperature below 15℃, especially where the maximum temperature does not exceed 30℃, such as Tibet, direct air cooling is preferred; for regions with lower average temperatures but equally pronounced periods of high and extremely low temperatures, such as the three northern regions of my country, indirect air cooling technology is preferred, which can achieve lower condensation temperatures in summer and meet winter antifreeze requirements; for regions with high average annual temperatures, water cooling technology is preferred, and for regions near rivers, lakes, and seas, open cooling is preferred; considering the working fluid condensation pressure and the time-varying characteristics of external environmental parameters, a working fluid with a normal boiling point close to the condensation temperature is preferred; for plateau regions with low environmental pressure and average temperature, a working fluid with a higher normal boiling point is preferred, allowing the condensation pressure to approach the environmental pressure and improving the system's energy output characteristics;

[0055] Equipment selection: Equipment selection and matching are mainly based on the parameters, material flow, and energy flow mentioned above. Shell-and-tube heat exchangers are preferred for geothermal water flow, as the geothermal water flows through the tubes, facilitating cleaning and maintenance of scale. Turbine selection is based on the working fluid's pressure ratio, inlet and outlet temperatures, working fluid flow rate, and power generation capacity. The influence of external time-varying parameters on material and energy flow, and their synergistic effect on turbine operating characteristics, should be emphasized. This is mainly reflected in the turbine's inlet temperature and pressure, condensing pressure, and working fluid flow rate. For situations with large pressure ratio variations, axial-flow turbines are preferred. For combustible working fluids, turbines with magnetic levitation seals integrated with generators are preferred. Pump selection mainly involves material flow and head, especially considering the influence of external time-varying parameters based on the pump's flow characteristic curve. Axial-flow pumps are preferred for reinjection pumps to ensure sufficient reinjection pressure margin. Finned-tube heat exchangers are preferred for air-cooled condensers, and shell-and-tube heat exchangers are preferred for water-cooled condensers.

[0056] For the selection and design of key equipment, considering the equipment's characteristics, characteristic curves, layout, and operating strategies, the response function relationship of key equipment parameters with time-varying boundary parameters is established. The variation patterns of heat exchanger temperature difference, heat flux density, and heat transfer coefficient along the flow path during the dynamic response of the heat exchanger are characterized. The parameters within the heat exchanger, along with the mass transport and energy transfer functions, are established. After heat exchange, the working fluid enters the turbine, and its parameters are affected by the time-varying characteristics of the boundary parameters, acting on the turbine. The condenser's response to the time-varying characteristics of the boundary parameters directly determines the turbine back pressure. Understanding the time-varying evolution of turbine inlet and outlet parameters and mass flow is crucial. Based on this, we establish the functional relationship between the full-condition turbine thermodynamic characteristics and working fluid parameters and material flow; we establish the relationship between the power consumption of production well pumps and reinjection pumps and the change in material flow, considering the time-varying characteristics of wellbore scaling, and understand its impact on production wellhead and reinjection pressure; we establish the influence of the time-varying characteristics of working fluid pump inlet and outlet parameters and material flow on power consumption; we establish the influence law of environmental boundary time-varying parameters on the operating parameters, material flow, and power consumption of the cooling system; from an overall perspective, we understand the evolution characteristics of parameters, material flow, and energy flow at each node; finally, based on the response characteristics of key equipment to time-varying parameter characteristics, we establish a function set.

[0057] For heat exchange equipment, the set of functions relating heat exchange load and time-varying boundary parameters is as follows:

[0058] h(x)=[h1(x1),h2(x2),h3(x3)…h n (x n )]

[0059] For turbines and generator sets, the set of functions relating power generation to time-varying boundary parameters is as follows:

[0060] w(x)=[w1(x1),w2(x2),w3(x3)…w n (x n )]

[0061] For energy-consuming equipment such as pumps, the set of functions relating power to time-varying boundary parameters is as follows:

[0062] g(x)=[g1(x1),g2(x2),g3(x3)…g n (x n )]

[0063] Where, x i To influence the time-varying parameters of the equipment, n is the number of boundary time-varying parameters involved in the equipment.

[0064] Understanding the covariant relationships between key equipment and processes specifically includes: the dependence of production wellhead parameters and reinjection parameters on the working fluid parameters of the evaporator and condenser; the covariant relationship between the working fluid parameters at the evaporator outlet and condenser inlet and the turbine output power; the covariant relationship between the condenser working fluid parameters, cooling system power consumption, and environmental parameters; and the covariant relationship between production well and reinjection parameters, system output power, and auxiliary equipment power consumption. These covariant relationships are quantitative, involving the conservation of material flow and energy flow, expressed by the function P(x i )-P(x j ) = 0 indicates that it serves as a constraint for optimization.

[0065] Based on the equipment characteristics, determine the constraint boundary functions for the operating parameters:

[0066] H(x i )-y i ≤0

[0067] Wherein, H(x) i ) represents the functional relationship of the corresponding parameters, y i These are the constraint boundaries for the corresponding parameters.

[0068] S3. Based on the time-varying response relationship of key equipment parameters with boundary parameters, establish a thermodynamic optimization objective function with the goal of maximizing net output work.

[0069] Based on the mastered parameter coordination and spatiotemporal evolution laws of material flow and energy flow, a thermodynamic optimization model of the geothermal generator unit is established. Based on the thermodynamic characteristics, parameter coordination laws and constraints of the above-mentioned equipment, the main parameters of the cycle are optimized with the goal of maximizing net output power, including working fluid evaporation pressure, turbine inlet temperature, condensation pressure, etc., and the optimization results are output to the next level of economic optimization module.

[0070] The output power considers both the response function of the turbine generator and the time-varying boundary parameters, and the response relationship function of the heat exchanger and the time-varying boundary parameters. The system power consumption considers both the response relationship function of the energy-consuming equipment and the time-varying boundary parameters, and the response relationship function of the heat exchanger and the time-varying boundary parameters. The thermodynamic optimization objective function is:

[0071]

[0072] The constraints are:

[0073] P(x i )-P(x j ) = 0

[0074] H(x i )-y i ≤0

[0075] S4. Calculate the total power generation of the system throughout its entire life cycle by combining the optimization results of the thermodynamic optimization objective function. Calculate the total power generation cost of the system throughout its entire life cycle based on the total power generation and total investment operating cost. Establish an economic optimization objective function with the goal of minimizing power generation cost.

[0076] An economic optimization model is established, based on the time-varying characteristics of parameters, to calculate the power generation throughout the entire life cycle using piecewise functions. The model considers economic factors such as loan interest rates, equipment reserve, design costs, and installation and construction costs to calculate the total investment and operating costs. Based on the time-varying characteristics of equipment operation, heat source and environmental parameters, and operating strategies, and considering the characteristics of different time periods, energy costs are calculated, specifically including geothermal water costs and plant power costs. Operating costs are calculated based on the geothermal power plant's operating personnel wages, maintenance costs, and office area maintenance and operating costs throughout the entire life cycle. Time-varying maintenance costs are mainly based on equipment operating characteristics and the overall plant operating strategy, involving daily equipment maintenance costs, spare parts (valves and instruments), costs incurred due to equipment aging and replacement, and working fluid storage and replenishment costs. For new energy power plants, incentives and subsidies related to environmental protection are deducted from the power generation costs.

[0077] For the entire life cycle, the power generation cost is calculated. The optimization parameters at the next higher level are evaluated based on minimizing the power generation cost. The main parameters are then optimized, and the optimized solution is fed back to the thermodynamic optimization model. The economic optimization objective function is...

[0078]

[0079] The constraints are:

[0080] P(x i )-P(x j ) = 0

[0081] H(x i )-y i ≤0

[0082] R(x i )-R i ≤0

[0083] D(x i )-D i ≤0

[0084] Among them, C F For all equipment investments, c F,i The unit price of a certain piece of equipment, f i The correlation function for this equipment should consider economic factors such as the number of devices (including spares), depreciation, and loan interest rates; C E For energy cost, c E,i Let m be the unit cost of a certain amount of energy consumed. i Let τ be the correlation function of mass flow rate or corresponding quantity, considering economic factors such as price fluctuations. i For the corresponding time-varying time period; C O For operating costs, C M To maintain costs; C A Income from environmental incentives and subsidies such as carbon emission reduction; R(x) i ) represents the safety constraints, R i And is the safety constraint boundary; D(x) i ) represents emission constraints, such as emissions of gases like H2S and SO2 from geothermal fluids, thermal emissions from cooling towers, wastewater discharge, and carbon emissions, D(x) i () represents the emission constraint boundary.

[0085] S5. When the optimization result of the economic optimization objective function cannot meet the requirements, readjust the equipment selection, and obtain the global optimization parameters through iteration and recursion.

[0086] When power generation costs are high, the design, selection, and parameter configuration of the equipment are improved, the boundaries of the operating parameters are adjusted accordingly, and the following related steps are repeated. Through continuous iteration, the global optimization parameters are gradually obtained, and the optimization scheme is output to minimize the power generation cost.

[0087] Based on the full life cycle operation optimization scheme, through big data mining, the optimal solution for key equipment is proposed from the characteristics of geothermal resource decay with the development years, power generation load prediction, and economic cost prediction. By considering the system's energy output characteristics, equipment energy consumption characteristics, operation and maintenance costs, and environmental protection requirements, the reverse design of the entire system is provided.

Claims

1. A method for optimizing the operation of a geothermal power generation system considering the time-varying characteristics of boundary parameters, characterized in that: Based on the collected geothermal resource data, local climate data, and hydrological condition data, the time-varying characteristics of the relevant boundary parameters for system operation are established. Equipment selection is based on the time-varying characteristics of system operation-related boundary parameters. Based on the design and operation characteristics of the key equipment in the selected equipment, the response relationship of the key equipment parameters with the time-varying boundary parameters is constructed. Based on the time-varying response relationship of key equipment parameters with boundary parameters, a thermodynamic optimization objective function is established with the goal of maximizing net output work. The power generation of the system throughout its entire life cycle is calculated by combining the optimization results of the thermodynamic optimization objective function. The power generation cost of the system throughout its entire life cycle is calculated based on the power generation of the system throughout its entire life cycle and the total investment and operating cost. An economic optimization objective function with the goal of minimizing the power generation cost is then established. When the optimization result of the economic optimization objective function fails to meet the requirements, the equipment selection is readjusted, and through iteration, the global optimization parameters are obtained step by step recursively. The boundary parameters include power generation load demand, environmental parameters, heat source parameters, and reinjection parameters; The time-varying response relationship of the key equipment parameters with respect to boundary parameters includes: A set of functions relating heat transfer load to time-varying boundary parameters ； A set of functions relating power generation to time-varying boundary parameters ； A set of functions relating the power of energy-consuming equipment to time-varying boundary parameters. ； wherein, n is the number of boundary time-varying parameters involved in the device; The thermodynamic optimization objective function is: ； W represents a power generation function; G represents a power consumption function; for the respective time-varying time period; The economic optimization objective function is: ； in, Invest in all equipment; The unit price of a certain piece of equipment; The correlation function for the equipment takes into account economic factors such as the number of standby devices, depreciation, and loan interest rates. Energy cost; The unit cost of a certain amount of energy consumed; For mass flow or the corresponding quantity, consider the economic factors of price fluctuations; For the corresponding time-varying time period; For operating costs, To maintain costs; For carbon emission environmental incentives and subsidies; W represents the electricity generation function.

2. The method of operating optimization of a geothermal power system taking into account time-variation of boundary parameters according to claim 1, characterized in that, The constraints of the thermodynamic optimization objective function include: The covariant relationship of parameters between key system equipment and processes is used as a constraint condition. The operating parameters constrain the boundary conditions based on the characteristics of the equipment.

3. The method of claim 1, wherein the method further comprises: The constraints of the economic optimization objective function include: The covariant relationship of parameters between key system equipment and processes is used as a constraint condition. Boundary conditions constrained by operating parameters determined based on equipment characteristics; Safety constraints; Emission constraints.

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