Energy-Material Co-conversion Method and System for Flue Gas SO2 Sulfuric Acid Production

Abstract

This invention relates to the field of non-ferrous metallurgical control technology, and discloses a method and system for energy-matter synergistic conversion in the SO2 sulfuric acid production process from flue gas. The method includes constructing a generalized thermodynamic control system by integrating a molten pool furnace, waste heat boiler, flue gas purification system, contact converter, absorption tower, and thermal network; establishing a dynamic balance model for sulfur and a cascaded energy-exertion utilization model, achieving precise tracking of sulfur in the slag phase, matte / crude metal phase, flue dust phase, and gas phase; employing multivariate coupled model predictive control, fusing a state observer and a predictive controller, and dynamically solving the optimal control law based on the rolling time-domain optimization principle; and dynamically optimizing weight allocation by defining an energy-matter synergistic index and embedding it into the objective function of the multivariate coupled model. This invention achieves efficient sulfur resource recovery, energy consumption optimization, dynamic load adaptation, and precise cascaded utilization of thermal energy throughout the entire process, improving system stability and economic benefits.

Description

Technical Field

[0001] This invention relates to the field of non-ferrous metal metallurgical control technology, and in particular to a method and system for energy-matter synergistic conversion in the SO2 sulfuric acid production process from flue gas. Background Technology

[0002] Non-ferrous metallurgy refers to the engineering discipline and industrial process of extracting and refining metals other than iron, chromium, and manganese, such as copper, aluminum, zinc, nickel, rare metals, and precious metals, from raw materials such as ores, concentrates, and secondary resources like scrap metals, through physical and chemical methods, to produce metals or compounds with specific properties and economic value. Molten pool smelting technology is currently the mainstream process in non-ferrous metallurgy and is widely used in the processing of sulfide ores. Under a strong oxidizing atmosphere and high-temperature conditions of 1100℃~1300℃, sulfur in sulfur-containing concentrates is oxidized to generate high-temperature flue gas rich in sulfur dioxide. This high-temperature flue gas is not only an important secondary resource for sulfuric acid production, i.e., a material flow; it also contains enormous thermal energy, i.e., an energy flow. Traditional process flows follow a series model of smelting matte production – waste heat recovery – flue gas purification – contact conversion – absorption for acid production. Although this series model constitutes a continuous pathway in terms of physical connection, the management of material flow and energy flow has long been disconnected in terms of process control and design concepts.

[0003] In addition, existing technologies have made some progress in energy saving and consumption reduction of unit equipment, such as high-efficiency waste heat boilers and the development of low-temperature catalysts. However, from a systems engineering perspective, the following core technical pain points exist: First, there is the challenge of nonlinear decoupling between material and energy flows: Metallurgical furnaces, waste heat boilers, and acid production units are controlled as independent units. The smelting end focuses solely on metal recovery and furnace stability, leading to significant fluctuations in sulfur dioxide concentration and flow rate in the flue gas with changes in feed. The acid production end, as a passive receiving link, cannot anticipate upstream fluctuations, causing the converter bed temperature to deviate from the optimal catalytic activity zone. This not only reduces the overall utilization rate of sulfur (conversion and absorption rates) but also results in unstable tail gas emissions. Simultaneously, the lack of unified planning for high-temperature flue gas sensible heat recovery and exothermic or chemical heat recovery from acid production reactions leads to the downgrading of high-grade heat energy and the direct dissipation of low-grade heat energy, resulting in low energy utilization efficiency.

[0004] Second, the lack of control over multivariable, strongly coupled characteristics: the flue gas sulfuric acid production process is a typical large-time-lag, nonlinear, multivariable, strongly coupled system. For example, changes in the oxygen enrichment rate of the smelting furnace not only alter the sulfur dioxide concentration or mass flow in the flue gas, but also change the flue gas temperature or energy flow, thus affecting the steam production of the waste heat boiler and the reaction equilibrium temperature rise of the subsequent converter. Traditional single-loop PID control strategies struggle to handle such complex cross-coupling relationships and lack optimization methods based on a global objective function.

[0005] Third, there is a lack of a full-process collaborative optimization model: existing control strategies are mostly limited to the PID regulation of individual equipment, such as boiler drum water level control and converter inter-layer temperature control. There is a lack of a top-level collaborative model that is cross-section and has sulfur element S and energy E as the core. There is a lack of mathematical description that can treat molten pool smelting, flue gas acid production and waste heat utilization as a unified whole. This makes it impossible to balance the contradiction between resource recovery rate, energy efficiency and equipment safety at the global level, and it is difficult to cope with the multi-objective optimization needs under complex working conditions.

[0006] In conclusion, there is an urgent need to develop a new technology that can break down barriers between production stages and achieve deep coupling and synergistic transformation of material and energy flows throughout the entire process of acid production from molten pool smelting flue gas, in order to meet increasingly stringent environmental regulations and the demand for energy conservation and carbon reduction. Summary of the Invention

[0007] The main objective of this invention is to provide a method and system for the coordinated energy-material conversion in the flue gas SO2 acid production process. This addresses the problem in the existing technology of lacking a coordinated control method that can simultaneously predict upstream disturbances, decouple the interactive effects of multiple variables, and implement feedforward compensation. This results in the inability to achieve deep coupling and coordinated conversion between the material flow and energy flow throughout the entire process, thus restricting further improvements in sulfur resource recovery rate and thermal energy cascade utilization efficiency.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for energy-material co-conversion in a flue gas SO2 acid production process, comprising: The entire process equipment is defined as a unified thermodynamic control body, and a dynamic balance model of sulfur element throughout its life cycle and an energy-exertion cascade utilization model are established. Key parameters are collected in real time through belt scales, grade analysis, flue gas analyzers and thermocouples to achieve accurate distribution and tracking of sulfur in the slag phase, matte phase, flue dust phase and gas phase, and to complete the digital mapping of flue gas sensible heat and chemical reaction heat. The design incorporates an MPC architecture that includes a state observer and a predictive controller, selecting coupled variables from the smelting and acid production sides. It incorporates a sulfur dioxide oxidation reaction kinetic model and a pseudo-one-dimensional plug flow reactor model, using the current temperature and partial pressure to predict the bed outlet conversion rate and temperature rise, and feedforward compensation to respond in advance to upstream load fluctuations. An energy-matter synergy index is defined and embedded as a reward term in the MPC objective function. Under the constraints of environmental emissions, catalyst temperature window and equipment safety, the optimal control law is solved by rolling time-domain optimization to balance the competitive relationship between sulfur recovery rate and thermal energy cascade utilization rate. Based on the MPC prediction results, the waste heat recovery grade is dynamically adjusted through the plant's energy interconnection network, and the underlying actuators receive DCS / PLC instructions to achieve closed-loop control; at the same time, a decoupling strategy is used to coordinate the interference between the steam drum water level and steam pressure.

[0009] As a further improvement of this invention, the molten pool smelting furnace, waste heat boiler, flue gas purification system, contact converter, absorption tower, and plant thermal network are collectively defined as a unified generalized thermodynamic control body. The molten pool smelting furnace inlet is used as the system input boundary, and the finished acid outlet, chimney exhaust outlet, and heat network interface are used as the output boundary. A dynamic balance model of the entire life cycle of sulfur and an energy-exergy cascade utilization model based on the first and second laws of thermodynamics are established. Key parameters such as the amount of material entering the furnace, sulfur-oxygen ratio, oxygen enrichment rate, and molten pool temperature are acquired in real time through belt scales, online / offline grade analysis, flue gas online analyzer, and thermocouple array, enabling precise distribution and tracking of sulfur in the slag phase, matte / crude metal phase, flue dust phase, and gas phase. Simultaneously, the digital mapping of flue gas sensible heat and chemical reaction heat is completed, providing a high-fidelity state-space description for collaborative control.

[0010] As a further improvement of the present invention, the process for accurately tracking the distribution of sulfur in the slag phase, matte / crude metal phase, flue dust phase, and gas phase includes the following steps: The amount of material fed into the furnace is continuously weighed by a belt scale, and the sulfur content and sulfur-oxygen ratio in the material are determined based on online / offline grade analysis data to obtain the total sulfur content and sulfur-oxygen ratio parameters. The measurement results of slag quantity and matte / crude metal quantity, combined with online / offline grade analysis, yield the sulfur content entering the slag and the sulfur content entering the matte / crude metal phase; at the same time, based on the dust collection volume of the dust collection system and the sulfur content of the flue dust, the sulfur content carried by the flue dust is determined, forming the sulfur distribution in the slag phase, matte phase and flue dust phase. The sulfur distribution in the slag phase, matte phase, and flue phase, along with the oxygen enrichment rate measured by the flow meter and the molten pool temperature measured by the thermocouple, are input into the nonlinear coupled prediction model of the gas phase sulfur flow rate to calculate the amount of sulfur entering the gas phase, thus completing the complete distribution tracking of sulfur elements in the slag phase, matte / crude metal phase, flue phase, and gas phase.

[0011] As a further improvement of the present invention, a multivariable model predictive control (MPC) system is designed, which includes a state observer and a predictive controller. The operating parameters of the smelting side and the operating parameters of the acid production side are selected as the coupled control variables. Based on the rolling time-domain optimization principle, the optimal control law is solved by minimizing the objective function. The predicted output vector includes the sulfur dioxide concentration in the chimney, the finished acid concentration, the total steam output, and the wall temperature of key equipment. A sulfur dioxide oxidation reaction kinetic model is built in, which uses the current temperature and gas partial pressure to predict the bed outlet conversion rate and temperature rise. The feedforward compensation mechanism responds in advance to the upstream smelting load fluctuations.

[0012] The smelting side operating parameters include oxygen enrichment rate, feed rate, and fuel supplementation heat, while the acid production side operating parameters include the quench air volume / cooling water volume of each section of the converter, circulating acid temperature, and boiler feed water volume.

[0013] As a further improvement of the present invention, the process of identifying time-varying parameters that consider catalyst activity decay in predicting future conversion rates and temperature rises using current temperature and partial pressure includes the following steps: The catalyst bed is discretized into multiple micro-volumes along the gas flow direction, with each micro-volume considered as a uniform reaction zone. Starting from the bed inlet, the instantaneous reaction rate of sulfur dioxide is calculated based on the gas temperature and partial pressure of each component within the current micro-volume. This reaction rate is divided by the sulfur dioxide inlet molar flow rate and then multiplied by the micro-volume volume to obtain the increment of the conversion rate within the micro-volume. The inlet conversion rate and the increments of each micro-volume are successively accumulated. After recursively extrapolating through all micro-volumes, the predicted value of the sulfur dioxide conversion rate at the bed outlet and the corresponding reaction temperature rise are finally obtained. At each sampling time, the measured value of sulfur dioxide conversion rate at the converter outlet is extracted and compared with the predicted value obtained by integral calculation to form the prediction error. The prediction error is multiplied by the gain coefficient determined by the covariance matrix of the previous time step and the current sensitivity vector to correct the estimated value of the activity decay factor of the previous time step, and the updated value of the activity decay factor of the current time step is obtained. At the same time, the covariance matrix is ​​updated using the same gain and sensitivity vector, and a forgetting factor is introduced to enhance the tracking ability of time-varying characteristics. The output activity decay factor is used to adjust the catalyst pre-exponential factor in real time to complete the online identification and correction of model parameters. The product of the activity decay factor and the initial pre-exponential factor forms the updated time-varying pre-exponential factor parameter, which is then substituted into the reaction rate formula to replace the original fixed pre-exponential factor, thus completing the online identification and correction of catalyst activity decay by the MPC prediction model.

[0014] As a further improvement of the present invention, a process for dynamically and adaptively adjusting the multi-objective weights in the energy-matter synergy index is defined and calculated in real time, including the following steps: Real-time acquisition of three types of external signals: sulfur dioxide concentration in chimney exhaust gas, carbon emission intensity per unit product, and real-time electricity price; normalization processing is performed on each type of signal. The three normalized coefficients are input into the fuzzy logic regulator; the fuzzy reasoning process includes input membership degree calculation, rule matching and weight synthesis, centroid method defuzzification, and outputs the sulfur utilization rate weight factor, energy efficiency weight factor and collaborative reward coefficient at the current moment; The three obtained parameters replace the corresponding fixed parameters in the original objective function to form a temporary objective function that reflects the current environmental protection-economic synergy requirements. Under the premise of meeting the hard constraints of environmental emissions, the catalyst activity temperature window, and the equipment safety limits, the MPC rolling optimization solver solves the optimal control increment vector with the objective function as the goal. The optimization results are sent down to the underlying actuators to realize closed-loop optimal control driven by environmental protection-economic synergy. The three steps are repeated at the next sampling time to form a real-time adaptive loop.

[0015] As a further improvement of the present invention, the current measured concentration is mapped to a relative position within the [low, high] range by using the normal operating low value of sulfur dioxide concentration in exhaust gas as the lower limit and the environmental emission limit as the upper limit, thus obtaining the environmental pressure coefficient; similarly, the upper and lower limits of carbon emission intensity are set according to the enterprise's carbon emission reduction target to obtain the carbon emission reduction urgency coefficient; and the economic incentive coefficient is obtained by mapping according to the range of electricity price fluctuations.

[0016] As a further improvement of the present invention, the preset fuzzy rules include: if the environmental pressure coefficient is too high or the exhaust gas is close to the limit, the sulfur utilization rate weight factor is forced to take the maximum value and the energy efficiency weight factor is taken to the minimum value, while the synergistic reward coefficient is reduced to prioritize meeting environmental constraints; if the economic incentive coefficient is too high or the electricity price is high, the energy efficiency weight factor is significantly increased, the sulfur utilization rate weight factor is appropriately reduced, and the synergistic reward coefficient is increased to guide the system to generate more electricity and recover more heat energy; if the carbon emission reduction urgency coefficient is too high, the energy efficiency weight and synergistic reward coefficient are increased under the premise of ensuring environmental compliance; if all three are in the middle range, a balanced mode is adopted, and each weight factor takes a middle value.

[0017] As a further improvement of this invention, the principle of temperature matching and tiered utilization is implemented: high-grade thermal energy in the 900~1100℃ flue gas section generates high-pressure steam for power generation or turbine driving; medium-grade thermal energy in the 600~900℃ converter heat exchange section generates medium-pressure steam for process heating or drying; low-grade thermal energy in the absorption tower uses circulating acid heat exchange for recovery and is used for boiler feedwater preheating, plant heating, or domestic hot water; the bottom-level actuators include oxygen-enriched spray gun regulating valves, feedwater regulating valves, converter cooling dampers, circulating acid cooling bypass valves, etc.; the waste heat boiler drum water level is designed to suppress false water levels.

[0018] To achieve the above objectives, the present invention also provides the following technical solution: An energy-material co-conversion system for flue gas SO2 acid production process, applied to the aforementioned energy-material co-conversion method for flue gas SO2 acid production process, wherein the energy-material co-conversion system for flue gas SO2 acid production process comprises: The intelligent control unit at the smelting source is used to actively adjust the oxygen enrichment rate and fuel quantity based on the soft measurement model and MPC feedforward control of sulfur distribution to the gas phase, and to smooth the fluctuations of sulfur dioxide concentration and heat load in flue gas. The cascade waste heat recovery unit is used to convert the sensible heat of flue gas into high and medium pressure steam in stages through unsteady heat load prediction and multivariate decoupled control, so as to realize the quantitative extraction of heat energy according to grade. The reaction-heat exchange coupled acid production unit is used to embed the SO2 oxidation reaction kinetic model. It adopts adaptive temperature control to force the catalyst bed to run along the optimal activity curve, so as to realize the instantaneous synergy of reaction exothermic and heat exchange. The plant energy interconnection network unit is used to perform peak shaving and valley filling and quality-based distribution of waste heat from acid production based on the perception and intelligent scheduling of the plant's energy demand, thereby eliminating the spatial and temporal mismatch of heat energy. The energy-matter co-conversion control center is used to construct a digital twin and calculate the co-conversion index Ψ in real time. It utilizes MPC and multi-objective optimization to solve for the optimal instruction and drive the entire process of material and energy flow in a double-closed automatic cruise.

[0019] To achieve the above objectives, the present invention also provides the following technical solution: An electronic device includes a processor and a memory coupled to the processor, the memory storing program instructions executable by the processor; when the processor executes the program instructions stored in the memory, it implements the energy-matter co-conversion method for flue gas SO2 acid production as described above.

[0020] To achieve the above objectives, the present invention also provides the following technical solution: A storage medium storing program instructions, which, when executed by a processor, implement an energy-matter co-conversion method for the flue gas SO2 acid production process as described above.

[0021] This invention integrates a smelting furnace, waste heat boiler, flue gas purification system, contact converter, absorption tower, and thermal network to construct a generalized thermodynamic control system. It establishes a dynamic balance model for sulfur and an energy-exergy cascade utilization model, combining real-time data acquisition and digital mapping to achieve precise tracking of sulfur in the slag phase, matte / crude metal phase, flue gas phase, and gas phase, as well as a high-fidelity state description of flue gas sensible heat and chemical reaction heat, providing a reliable foundation for coordinated control. A multivariate coupled model predictive control is employed, fusing a state observer and a predictive controller. Smelting-side operating parameters and acid-producing-side operating parameters are selected as control variables, and the optimal control law is dynamically solved based on the rolling time-domain optimization principle. The built-in sulfur dioxide oxidation reaction kinetic model, combined with a feedforward compensation mechanism, effectively addresses upstream load fluctuations, ensuring the converter bed temperature remains stable within the catalyst's optimal activity range, thus improving the overall system reaction efficiency and stability. By defining an energy-matter synergy index and embedding it into the objective function of the multivariate coupled model, the weight allocation is dynamically optimized to balance the competitive relationship between sulfur resource recovery rate and thermal energy cascade utilization, achieving optimal matching of overall system performance. Simultaneously, the energy interconnection network is used to adjust the waste heat recovery grade and heat network load distribution to ensure the efficient utilization of heat energy of various grades, such as high-temperature flue gas power generation, medium-temperature steam process heat tracing, and low-temperature thermal energy plant preheating. Combined with DCS / PLC closed-loop control and decoupling strategies, interference from steam drum water level and steam pressure is eliminated, improving the safety and economy of system operation. Attached Figure Description

[0022] Figure 1This is a schematic flowchart of one embodiment of the energy-material synergistic conversion method for flue gas SO2 acid production according to the present invention; Figure 2 This is a schematic diagram of the steps in an embodiment of the energy-material synergistic conversion method for flue gas SO2 acid production in the present invention to achieve precise distribution and tracking of sulfur in the slag phase, matte / crude metal phase, flue dust phase and gas phase. Figure 3 This is a schematic diagram of the steps in the energy-material synergistic conversion method for flue gas SO2 acid production process of the present invention, which uses the current temperature and partial pressure to predict the future conversion rate and identifies time-varying parameters that take into account the decay of catalyst activity in the temperature rise. Figure 4 This is a flowchart illustrating the steps of defining and dynamically adaptively adjusting the multi-objective weights in the energy-material synergy index in real time, as an embodiment of the energy-material synergy index for SO2 acid production from flue gas according to the present invention. Figure 5 This is a schematic diagram of the functional modules of an embodiment of the energy-material synergistic conversion system for flue gas SO2 acid production according to the present invention; Figure 6 This is a schematic diagram of an embodiment of the energy-material synergistic conversion system for flue gas SO2 acid production according to the present invention; Figure 7 This is an MPC model predictive control architecture diagram of an embodiment of the energy-material synergistic conversion system for flue gas SO2 acid production in this invention; Figure 8 This is a schematic diagram of a cascade waste heat recovery network in an embodiment of the flue gas SO2 acid production process energy-material synergistic conversion system of the present invention; Figure 9 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention; Figure 10 This is a schematic diagram of the structure of one embodiment of the storage medium of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] The terms "first," "second," and "third" in this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of those features. In the description of this invention, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this invention are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indication changes accordingly. Furthermore, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0025] References to embodiments herein mean that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0026] This invention proposes a method and system for the synergistic conversion and utilization of energy and matter in the flue gas treatment process of molten pool smelting (including top-blown, side-blown, bottom-blown, and bath smelting processes). It belongs to the integrated technology category of metallurgical flue gas sulfuric acid production, industrial waste heat cascade recovery, and system-level optimization control of complex industrial processes. It is particularly suitable for copper, lead, zinc, nickel, and other heavy non-ferrous metal smelting enterprises to achieve efficient sulfur resource recovery and overall process energy efficiency improvement. This method achieves the above objectives through the following three core technical features: (1) Construct a double closed-loop mechanism model to realize the digital mapping of material and energy flow; take the molten pool smelting furnace, waste heat boiler, flue gas purification system, contact converter, absorption tower and plant thermal network as a unified generalized thermodynamic control body, establish a dynamic balance model of sulfur element throughout the entire life cycle and an energy-exertion cascade utilization model based on the first and second laws of thermodynamics; the sulfur element balance model realizes accurate tracking of sulfur resources from the furnace to the finished acid and tail gas emission through nonlinear coupling prediction of the amount of material entering the furnace, the distribution of sulfur in the slag phase / crystal phase / dust phase and gas phase sulfur; the energy model quantifies the source of sensible heat of flue gas and heat of sulfur dioxide oxidation reaction, and determines the effective recovery amount of high-pressure steam of waste heat boiler, medium-pressure steam of conversion section and low-temperature hot water of absorption tower according to high, medium and low grades; the two models jointly construct a digital mapping of material flow and energy flow throughout the entire process, providing a high-fidelity state space description for collaborative control.

[0027] (2) To achieve predictive collaborative control and solve the problems of large time lag and multivariate coupling, a model predictive control algorithm is adopted, which uses the smelting side operating parameters (oxygen enrichment rate, feed rate, fuel supplementation heat) and the acid production side operating parameters (cooling air volume / cooling water volume of each section of the converter, circulating acid temperature, boiler feedwater volume) as coupled control variables. The algorithm incorporates a sulfur dioxide oxidation reaction kinetic model and a pseudo-one-dimensional plug flow reactor model. By using the current catalyst bed temperature and gas partial pressure, the conversion rate and temperature rise at the bed outlet are predicted by axial integration, thereby sensing the upstream smelting load fluctuations in advance and adjusting the cooling intensity and feedwater parameters through a feedforward compensation mechanism. This strategy effectively solves the control instability problem caused by large time lag and strong multivariate coupling in the smelting process, ensuring that the converter bed temperature is always maintained in the optimal activity range of the catalyst, and significantly improving the sulfur conversion efficiency and the system's anti-disturbance capability.

[0028] (3) Quantify synergistic efficiency and balance the competitive relationship between sulfur recovery and thermal energy cascade utilization. Define an energy-material synergy index, which is the weighted product of sulfur comprehensive utilization rate and total energy utilization efficiency. The weighting factor is dynamically adjusted according to the production strategy (environmental protection priority or energy conservation priority). Embed the synergy index as a reward item into the multi-objective optimization objective function of model predictive control. Under the premise of meeting the hard constraints of environmental emission, catalyst temperature window and equipment safety limit, the optimal control law is solved by rolling time domain optimization to achieve the simultaneous maximization of sulfur resource recovery rate and thermal energy cascade utilization efficiency. It overcomes the defects of traditional single-objective control that suffer from one aspect at the expense of another, transforms the complex multi-objective trade-off into a real-time mathematical optimization problem, and finally achieves the dual improvement of economic benefits and environmental protection indicators.

[0029] like Figure 1 As shown, this embodiment provides an example of a method for the coordinated energy-material conversion in the flue gas SO2 acid production process. In this embodiment, the method for the coordinated energy-material conversion in the flue gas SO2 acid production process specifically includes the following steps: Step S1: Define the molten pool furnace, waste heat boiler, flue gas purification system, contact converter, absorption tower, and plant thermal network as a unified generalized thermodynamic control system, with the molten pool furnace inlet as the system input boundary and the finished acid outlet, chimney exhaust outlet, and heat network interface as the output boundary; establish a dynamic balance model of the entire life cycle of sulfur and an energy-exergy cascade utilization model based on the first and second laws of thermodynamics; acquire key parameters such as the amount of material fed into the furnace, sulfur-oxygen ratio, oxygen enrichment rate, and molten pool temperature in real time through belt scales, online / offline grade analysis, flue gas online analyzer, and thermocouple array to achieve precise distribution and tracking of sulfur in the slag phase, matte / crude metal phase, flue dust phase, and gas phase; and simultaneously complete the digital mapping of flue gas sensible heat and chemical reaction heat to provide a high-fidelity state-space description for collaborative control; The dynamic equilibrium model for the entire life cycle of sulfur includes: A dynamic equilibrium model for sulfur resources throughout the entire process is established, based on the following dynamic mass balance equation: Let the system time be... t The total sulfur input mass flow rate is The allocation relationship is as follows:

[0030] in: The total sulfur content (kg / h) entering the furnace is obtained through belt weighing and raw material composition analysis data. The amounts of sulfur entering the slag and the matte / crude metal phase are respectively obtained through slag / matte quantity measurement and online / offline grade analysis; The amount of sulfur carried by the flue gas in the dust is determined by the amount of dust collected and the sulfur content of the dust. This refers to the amount of sulfur entering the gas phase, mainly existing in the form of SO2 and a small amount of SO3.

[0031] Gas phase sulfur flow rate The nonlinear coupling prediction model with process parameters is as follows:

[0032] in: It is the pure lag time (h) of the smelting reaction. The oxygen enrichment rate or oxygen equivalent ratio of the blower is dimensionless and is measured by a flow meter. Represents the molten pool temperature (°C), measured by a thermocouple or infrared thermometer; This indicates the sulfur-to-oxygen ratio of the material fed into the furnace; it is dimensionless. All of these are empirical coefficients obtained through regression analysis of historical operational big data, with values ​​of 0.5, 2, 1.5, and 1, respectively. sulfur utilization rate of acid production system Defined as:

[0033] in The yield of finished acid was measured by an electromagnetic flowmeter. M This represents the molar mass of the corresponding substance.

[0034] An energy-exergy cascade utilization model is established, based on the first and second laws of thermodynamics, to determine the system's total energy utilization efficiency. Defined as:

[0035] Among them, the energy sources are: Sensible heat of flue gas :

[0036] in, The mass flow rate of flue gas is (kg / h). The average isobaric specific heat capacity (kJ / kg·K) This refers to the inlet flue gas temperature of the waste heat boiler. The ambient reference temperature.

[0037] Heat of chemical reaction :

[0038] in, The first one representing the converter Sectional bed layer, The enthalpy of SO2 oxidation reaction (kJ / mol) The SO2 molar flow rate (mol / h) participating in this reaction is calculated using the inlet and outlet concentration difference.

[0039] Effective energy recovery items include :

[0040] in, (High-grade) refers to the enthalpy increase of high-pressure steam generated by waste heat boilers (used for power generation / turbine drive); (Medium grade) refers to medium-pressure steam or superheated hot water (used for process heating) generated by the heat exchanger in the conversion section. (Low grade) refers to the low-temperature heat energy recovered by the circulating acid heat exchanger in the absorption tower (used for heating and preheating boiler feedwater).

[0041] Step S2: Design a multivariable model predictive control (MPC) system that includes a state observer and a predictive controller. Select the smelting side operating parameters and the acid production side operating parameters as the coupled control variables. Based on the rolling time-domain optimization principle, solve the optimal control law by minimizing the objective function. The predicted output vector includes the sulfur dioxide concentration in the chimney, the finished acid concentration, the total steam output, and the wall temperature of key equipment. Incorporate a sulfur dioxide oxidation reaction kinetic model to predict the bed outlet conversion rate and temperature rise using the current temperature and gas partial pressure. Respond in advance to upstream smelting load fluctuations through a feedforward compensation mechanism to solve the problems of large lag and nonlinear coupling, and ensure that the converter bed temperature is always maintained within the optimal activity range of the catalyst. Among them, the smelting side operating parameters include oxygen enrichment rate, feed rate, and fuel supplementation heat, while the acid production side operating parameters include the cold quench air volume / cooling water volume of each section of the converter, circulating acid temperature, and boiler feed water volume. To achieve accurate prediction and control of the converter bed temperature, a mechanism-based SO2 oxidation reaction kinetic model is incorporated into the contact oxidation reaction kinetic model; for the first... Sectional bed, its reaction rate The description is as follows:

[0042] Explanation of formula variables and methods for obtaining parameters: Indicates the first The sulfur dioxide oxidation reaction rate in the catalyst bed ; The pre-exponential factor of this catalyst is related to the active components (such as V2O5 content), geometry, and packing method of the catalyst. Indicates the activation energy of the reaction; Describing the ideal gas constant ; Indicates the local gas temperature in the bed The data is collected in real time by a multi-point thermocouple array inside the converter. Represents the partial pressures of each component in the reaction system. Based on intake component analysis values ​​and current conversion rate Real-time calculation; Indicates the reaction order, usually Take a value of 0.8 to 1.0. Take a value of 0.2~0.4; This represents the reaction equilibrium constant at that temperature, which can be obtained by looking up a table in a standard thermodynamics database.

[0043] The formula is used to calculate at the current temperature The instantaneous consumption rate of SO2 under specific concentration conditions. Using a pseudo-one-dimensional plug flow reactor model, the conversion rate and temperature rise at the bed outlet can be solved by integrating along the axial direction:

[0044] in For catalyst volume, This refers to the inlet molar flow rate. The MPC controller uses this model to predict the impact of future changes in cooling airflow. The changes are analyzed to predict the final conversion rate and thus find the optimal temperature control path.

[0045] Step S3: Define and calculate the energy-matter synergy index in real time as a quantitative representation of the overall system performance. It is embedded in the reward term of the objective function of the multivariate coupled model, guides the weight allocation of each objective function in multi-objective optimization, balances the potential competitive relationship between sulfur resource recovery rate and thermal energy cascade utilization efficiency, and achieves global optimal synergy. Energy-Matter Synergy Index To overcome the limitations of a single indicator, a collaborative evaluation function is constructed:

[0046] in As a weighting factor, it is increased according to the production strategy, such as prioritizing environmental protection. a Prioritizing energy conservation will increase b Dynamically set; The multivariable coupled optimization strategy based on MPC is the key difference between this invention and existing technologies. The following multi-objective optimization control problem is constructed: Objective Function J In the prediction time domain and control time domain Minimize the objective function:

[0047] in, The predicted output vector includes: sulfur dioxide concentration in the chimney, finished acid concentration, total steam output, and wall temperature of key equipment. Set the desired value trajectory; To control the incremental vector, including: oxygen enrichment rate Fuel quantity Boiler feedwater flow rate; Cooling air flow rate / cooling water flow rate for each section of the converter; It is a weighting matrix used to balance tracking error and control motion amplitude; This is the synergy index reward item; the negative sign indicates maximizing the synergy index.

[0048] Constraints: (Environmental protection constraints); (Catalyst activity temperature window constraint); (Equipment safety pressure constraints); (Physical limit of the executing agency).

[0049] Step S4: Based on the prediction results of the multivariate coupling model, dynamically adjust the waste heat recovery grade and the heat network load distribution through the plant energy interconnection network; the bottom actuator receives the control commands issued by the DCS / PLC to realize closed-loop control; at the same time, the multivariate coupling model decoupling strategy is used to coordinate the mutual interference between the waste heat boiler drum water level and steam pressure to ensure the safety and economy of the entire process of energy cascade utilization. Specifically, the system follows the principles of temperature matching and tiered utilization: high-grade thermal energy from the 900-1100℃ flue gas section is prioritized to generate high-pressure steam for power generation or turbine driving; medium-grade thermal energy from the 600-900℃ converter heat exchange section generates medium-pressure steam for process heating or drying; low-grade thermal energy from the absorption tower circulating acid heat exchange is recovered for boiler feedwater preheating, plant heating, or domestic hot water; the underlying actuators include oxygen-enriched spray gun regulating valves, feedwater regulating valves, converter cooling dampers, and circulating acid cooling bypass valves; the waste heat boiler drum water level is designed to suppress false water level readings. The DCS (Distributed Control System) and PLC (Programmable Logic Controller) serve as the underlying actuators, receiving optimization instructions from the host computer. Preferably, this embodiment integrates the smelting furnace, waste heat boiler, flue gas purification system, contact converter, absorption tower, and thermal network to construct a generalized thermodynamic control system. A dynamic balance model for sulfur and an energy-exergy cascade utilization model are established. Combined with real-time data acquisition and digital mapping, this achieves precise tracking of sulfur in the slag phase, matte / crude metal phase, flue gas phase, and gas phase, as well as a high-fidelity state description of flue gas sensible heat and chemical reaction heat, providing a reliable foundation for coordinated control. Multivariate coupled model predictive control is employed, fusing a state observer and a predictive controller. Smelting-side operating parameters and acid-producing-side operating parameters are selected as control variables, and the optimal control law is dynamically solved based on the rolling time-domain optimization principle. The built-in sulfur dioxide oxidation reaction kinetic model, combined with a feedforward compensation mechanism, effectively addresses upstream load fluctuations, ensuring the converter bed temperature remains stable within the catalyst's optimal activity range, thus improving the overall system reaction efficiency and stability. By defining an energy-matter synergy index and embedding it into the objective function of the multivariate coupled model, the weight allocation is dynamically optimized to balance the competitive relationship between sulfur resource recovery rate and thermal energy cascade utilization, achieving optimal matching of overall system performance. Simultaneously, the energy interconnection network is used to adjust the waste heat recovery grade and heat network load distribution to ensure the efficient utilization of heat energy of various grades, such as high-temperature flue gas power generation, medium-temperature steam process heat tracing, and low-temperature thermal energy plant preheating. Combined with DCS / PLC closed-loop control and decoupling strategies, interference from steam drum water level and steam pressure is eliminated, improving the safety and economy of system operation.

[0050] In summary, this embodiment achieves efficient recovery of sulfur resources, energy consumption optimization, dynamic load adaptation, and precise cascade utilization of thermal energy throughout the entire process, thereby improving system stability and economic benefits.

[0051] Furthermore, such as Figure 2 As shown, step S1, which involves accurately tracking the distribution of sulfur in the slag phase, matte / crude metal phase, flue dust phase, and gas phase, specifically includes the following steps: Step S11: Continuously weigh the amount of material entering the furnace using a belt scale, and determine the sulfur content and sulfur-oxygen ratio in the material entering the furnace based on online / offline grade analysis data to obtain the total sulfur content and sulfur-oxygen ratio parameters entering the furnace. Step S12: The measurement results of slag quantity and matte / crude metal quantity are combined with online / offline grade analysis to obtain the sulfur quantity entering the slag and the sulfur quantity entering the matte / crude metal phase; at the same time, the sulfur quantity carried by the flue dust is determined based on the dust collection volume of the dust collection system and the sulfur content of the flue dust, forming the sulfur distribution in the slag phase, matte phase and flue dust phase. Step S13: Input the sulfur distribution in the slag phase, matte phase, and flue gas phase, along with the oxygen enrichment rate measured by the flow meter and the molten pool temperature measured by the thermocouple, into the nonlinear coupled prediction model of the gas phase sulfur flow rate to calculate the amount of sulfur entering the gas phase, thus completing the complete distribution tracking of sulfur elements in the slag phase, matte / crude metal phase, flue gas phase, and gas phase.

[0052] Preferably, this embodiment establishes a closed-loop tracking system for sulfur elements throughout the entire process by real-time monitoring and distribution metering of sulfur content in the furnace feed material, precise determination of sulfur content in multiphase products, and model-based prediction of gas phase sulfur flow rate. Visual control of sulfur flow direction: Dynamic calibration of the amount of sulfur fed into the furnace is achieved based on the linkage between a belt scale and an online analyzer; combined with dual-dimensional detection of the weight and grade of the slag / matte phase, a quantitative migration path of sulfur in the condensed phase is established. Synergistic analysis of multiphase equilibrium: Direct measurement of sulfur content in flue dust and reverse calculation of sulfur content in the gas phase form complementary verification, ensuring the closure of the sulfur mass conservation relationship during smelting and providing thermodynamic and kinetic basis for process optimization. Coupled optimization of key parameters: Oxygen enrichment rate and molten pool temperature are used as input variables for the gas phase prediction model, revealing the sulfur volatilization mechanism under the synergistic effect of oxygen potential and temperature, providing a control benchmark for reducing sulfur dispersion loss.

[0053] In summary, this embodiment achieves a quantitative description of the migration law of sulfur in a gas-solid-liquid multiphase system, supporting the accurate evaluation of sulfur distribution efficiency and the scientific control of process parameters in the smelting process.

[0054] Furthermore, such as Figure 3As shown, step S2, which uses the current temperature and partial pressure to predict future conversion rates and identifies time-varying parameters that consider catalyst activity decay during temperature rise, specifically includes the following steps: Step S21: Discretize the catalyst bed into multiple micro-element volumes along the gas flow direction, with each micro-element considered as a uniform reaction zone; starting from the bed inlet, calculate the instantaneous reaction rate of sulfur dioxide based on the gas temperature and partial pressure of each component within the current micro-element; divide this reaction rate by the sulfur dioxide inlet molar flow rate, and then multiply by the micro-element volume to obtain the increment of conversion rate within the micro-element; successively accumulate the inlet conversion rate and the increment of each micro-element, and after recursively extrapolating through all micro-element layers, finally obtain the predicted value of sulfur dioxide conversion rate at the bed outlet and the corresponding reaction temperature rise; Step S22: At each sampling time, extract the measured value of sulfur dioxide conversion rate at the converter outlet and compare it with the predicted value obtained by integral calculation to form the prediction error; multiply the prediction error by the gain coefficient determined by the covariance matrix of the previous time and the current sensitivity vector to correct the estimated value of the activity decay factor of the previous time and obtain the updated value of the activity decay factor of the current time; at the same time, update the covariance matrix using the same gain and sensitivity vector, and introduce a forgetting factor to enhance the tracking ability of time-varying characteristics; the output activity decay factor is used to adjust the catalyst pre-exponential factor in real time to complete the online identification and correction of model parameters; Step S23: The product of the activity decay factor and the initial pre-exponential factor is used to form the updated time-varying pre-exponential factor parameter, which is then substituted into the reaction rate formula to replace the original fixed pre-exponential factor, thus completing the online identification and correction of catalyst activity decay by the MPC prediction model.

[0055] Preferably, this embodiment establishes a precise compensation mechanism for catalyst activity decay by combining dynamic discretization modeling with real-time parameter correction, achieving adaptive prediction of reaction conversion rate under unsteady-state conditions. The infinitesimal volume method quantifies the conversion rate increment at each spatial node by piecewise integration of the reaction rate, accurately capturing the influence of the axial temperature-concentration gradient on the reaction process and improving the prediction resolution of temperature rise and conversion rate. Based on feedback correction of real-time outlet conversion rate error, and in conjunction with dynamic adjustment of the covariance matrix, the time-varying trajectory of the activity decay factor is adaptively tracked, eliminating model mismatch problems caused by catalyst deactivation. By updating the pre-exponential factor and reconstructing the reaction rate equation, it is ensured that the MPC model parameters evolve synchronously with the actual catalyst activity, so that the predicted temperature rise and conversion rate always conform to the actual kinetic state of the current reactor.

[0056] In summary, this embodiment establishes a mapping relationship between catalyst activity decay in the reactor and macroscopic reaction indicators, achieving continuous accuracy in predicting conversion rate and temperature rise under dynamic operating conditions, and providing data support for catalyst efficiency evaluation and regeneration timing determination.

[0057] Furthermore, the process of updating the covariance matrix using the same gain and sensitivity vector in step S22 specifically includes the following steps: Step S221: Starting from the covariance matrix stored in the previous time step, extract the sensitivity vector or a column vector at the current time step; multiply the transpose of the sensitivity vector by the covariance matrix to obtain a row vector; then multiply the row vector by the sensitivity vector to obtain a scalar; add a unit value to the scalar and perform a reciprocal operation to obtain the scaling factor; multiply the covariance matrix by the sensitivity vector to obtain another column vector; multiply the column vector by the scaling factor to obtain the gain vector at the current time step. Step S222: Perform an outer product between the calculated gain vector and the transpose of the current sensitivity vector, i.e., multiply the column vector by the row vector to form a square matrix with the same dimension as the covariance matrix; subtract the square matrix from the identity matrix of the same dimension to obtain the difference matrix; then multiply the covariance matrix of the previous time step by the difference matrix to obtain the intermediate covariance matrix. Step S223: Take a constant value between 0.95 and 0.99 as the forgetting factor, divide each element in the obtained intermediate covariance matrix by the forgetting factor to obtain the updated covariance matrix, and store it for recursive calculation at the next sampling time.

[0058] Preferably, this embodiment constructs an adaptive covariance update mechanism for time-varying parameter identification, realizing dynamic tracking of catalyst activity decay and continuous optimization of model prediction accuracy; noise-resistant parameter estimation: by iterative scaling of the sensitivity vector and covariance matrix, the optimal gain vector is generated, significantly suppressing the interference of conversion rate measurement noise on the identification of activity factors; gradual fading memory of historical data: the introduction of the forgetting factor gradually weakens the weight of old data when updating the covariance matrix, strengthens the ability to capture recent process features, and ensures the model's rapid response to catalyst activity mutations; numerical stability of recursive calculation: the combined update of outer product operation and difference matrix avoids ill-conditioned expansion or contraction of the covariance matrix, maintaining the positive definiteness and boundedness of the error covariance during parameter correction.

[0059] In summary, this embodiment establishes a two-way coupled feedback between the time-varying characteristics of the catalyst and the reaction state, providing a robust online identification tool for dynamic optimization of the reactor under complex operating conditions.

[0060] Furthermore, such as Figure 4 As shown, step S3, which defines and calculates in real time the dynamic adaptive adjustment of multi-objective weights in the energy-matter synergy index, specifically includes the following steps: Step S31: Real-time acquisition of three types of external signals: sulfur dioxide concentration in chimney exhaust gas, carbon emission intensity per unit product, and real-time electricity price; normalization processing is performed on each type of signal: taking sulfur dioxide concentration in exhaust gas as an example, with its normal operating low value as the lower limit and the environmental emission limit as the upper limit, the current measured concentration is mapped to the relative position within the [low, high] interval to obtain the environmental pressure coefficient; similarly, the upper and lower limits of carbon emission intensity are set according to the enterprise's carbon emission reduction target to obtain the carbon emission reduction urgency coefficient; based on the range of electricity price fluctuations, the economic incentive coefficient is obtained. Step S32: Input the three normalized coefficients into the fuzzy logic regulator; the preset fuzzy rules include: if the environmental pressure coefficient is too high or the exhaust gas is close to the limit, then force the sulfur utilization rate weight factor to take the maximum value and the energy efficiency weight factor to take the minimum value, while reducing the collaborative reward coefficient to prioritize meeting environmental constraints; if the economic incentive coefficient is too high or the electricity price is high, then significantly increase the energy efficiency weight factor, appropriately reduce the sulfur utilization rate weight factor, and increase the collaborative reward coefficient to guide the system to generate more electricity and recover more heat energy; if the carbon emission reduction urgency coefficient is too high, then under the premise of ensuring environmental compliance, moderately increase the energy efficiency weight and collaborative reward coefficient to promote the cascade utilization of heat energy to reduce external energy consumption; if all three are in the middle range, then adopt the balance mode, and each weight factor takes a middle value; the fuzzy reasoning process includes input membership degree calculation, rule matching and weight synthesis, centroid method defuzzification, and finally outputs the sulfur utilization rate weight factor, energy efficiency weight factor and collaborative reward coefficient at the current moment; Step S33: Replace the corresponding fixed parameters in the original objective function with the three obtained parameters to form a temporary objective function that reflects the current environmental protection-economic synergy requirements; the MPC rolling optimization solver, under the premise of meeting the hard constraints of environmental emissions, catalyst activity temperature window and equipment safety limits, aims to minimize this objective function and solve for the optimal control increment vector, including oxygen enrichment rate adjustment, fuel heat supplementation, quench air volume, boiler feedwater volume, etc.; the optimization results are sent down to the bottom-level actuators, such as oxygen enrichment nozzle regulating valve, quench air damper, feedwater regulating valve, etc., to achieve closed-loop optimal control driven by environmental protection-economic synergy; the above three steps are repeated at the next sampling time to form a real-time adaptive loop.

[0061] Preferably, this embodiment establishes a multi-objective weight dynamic adaptive adjustment mechanism, enabling real-time coordinated optimization of environmental constraints, economic benefits, and carbon emission reduction targets. This mechanism, centered on a fuzzy logic regulator, dynamically adjusts the weight allocation of sulfur utilization rate, energy efficiency, and synergistic rewards through quantized mapping and rule matching of three key external signals. At the real-time optimization level, the MPC controller solves for the optimal control quantity that satisfies multiple constraints based on current operating conditions, ensuring the system maximizes economic benefits while maintaining emission compliance. This process creatively solves the shortcomings of traditional fixed-weight optimization models in handling operating condition fluctuations, achieving dynamic response of the control strategy through a rolling updated temporary objective function. From a system performance perspective, this method simultaneously achieves optimization effects in three dimensions: environmental protection, ensuring stable compliance of exhaust emissions through dynamic reinforcement of sulfur utilization rate weight; energy efficiency, flexibly adjusting energy recovery priority based on electricity price signals to improve overall system energy efficiency; and emission reduction, promoting the cascade utilization of thermal energy through the feedback effect of the carbon emission reduction urgency coefficient, reducing the carbon emission intensity per unit product. The entire control process forms a closed-loop optimization structure with self-learning characteristics, achieving dynamic balance among different objectives. The specific technical parameters will be automatically matched to the optimal combination based on real-time operating conditions, so that the system always operates at the operating point with the best overall performance.

[0062] like Figure 5 As shown, this embodiment also provides an embodiment of an energy-material co-conversion system for the flue gas SO2 acid production process. The principle is described in the appendix. Figure 6 ; Figure 7 Predictive control architecture diagram without MPC model Figure 8 A schematic diagram of a non-cascaded waste heat recovery network; In this embodiment, the flue gas SO2 acid production process energy-material synergistic conversion system is applied to the flue gas SO2 acid production process energy-material synergistic conversion method as described in the above embodiment. The flue gas SO2 acid production process energy-material synergistic conversion system includes a smelting source intelligent control unit, a cascaded waste heat recovery unit, a reaction-heat exchange coupled acid production unit, a plant area energy interconnection network unit, and an energy-material synergistic conversion control center that are connected in sequence by electrical connection. Among them, the intelligent control unit at the smelting source is used to actively adjust the oxygen enrichment rate and fuel quantity based on the soft measurement model and MPC feedforward control of sulfur distribution to the gas phase, and to smooth the fluctuations of sulfur dioxide concentration and heat load in flue gas; the cascade waste heat recovery unit is used to convert the sensible heat of flue gas into high and medium pressure steam in stages through unsteady heat load prediction and multivariate decoupling control, so as to realize the quantitative extraction of heat energy according to grade; the reaction-heat exchange coupled acid production unit is used to embed the SO2 oxidation reaction kinetic model, and adopts adaptive temperature control to force the catalyst bed to run along the optimal activity curve, so as to realize the real-time synergy of reaction exothermic and heat exchange; the plant area energy interconnection network unit is used to peak and valley filling and quality-based distribution of acid production waste heat based on the perception and intelligent scheduling of the energy demand of the whole plant, so as to eliminate the temporal and spatial mismatch of heat energy; the energy-material synergistic conversion control center is used to construct a digital twin and calculate the synergistic index Ψ in real time, and use MPC and multi-objective optimization to solve the optimal command in a rolling manner, driving the double closed automatic cruise of the entire process material flow and energy flow.

[0063] Preferably, in terms of raw material control, this embodiment utilizes soft measurement and model prediction control of sulfur distribution in the intelligent control unit at the smelting source to achieve stable output of sulfur dioxide concentration and heat load in flue gas, reducing fluctuations in subsequent processes. In the heat recovery stage, unsteady heat load prediction and multivariate decoupling control technology from a cascade waste heat recovery unit are employed to accurately extract and convert heat energy of different grades, improving waste heat recovery efficiency and energy quality utilization. In the core acid production process, the reaction-heat exchange coupling unit maintains the optimal catalyst activity state through an adaptive temperature control strategy, achieving a dynamic balance between the exothermic chemical reaction and heat exchange processes, thus improving reaction efficiency and heat utilization. At the energy distribution level, the plant's energy interconnection network unit uses intelligent scheduling algorithms to optimize the spatiotemporal distribution of heat energy throughout the plant, solving supply and demand matching problems and achieving rational energy allocation and cascade utilization. The system control center constructs a digital twin model, calculates and optimizes the synergy index in real time, and coordinates the operating parameters of each unit through multi-objective dynamic optimization to achieve overall optimal material conversion and energy utilization, achieving adaptive adjustment and closed-loop operation of the process. This embodiment achieves multiple objectives, including efficient conversion of sulfur resources, cascade utilization of thermal energy, enhanced reaction process, optimized energy network, and global coordinated control, significantly improving the energy utilization efficiency, process stability, and environmental benefits of the acid production process.

[0064] The intelligent control unit at the smelting source in this embodiment is the source of material and energy flow. Its core task is to transform traditional experience-based operation into model-based feedforward control. Physical components include: the molten pool furnace body, top-blown / side-blown / bottom-blown nozzles, oxygen-enriched lances controlled by a high-precision mass flow controller, and a quantitative feeding system equipped with a weighing sensor.

[0065] Data input: Offline / laboratory data: concentrate grade (S, Cu / Pb / Zn, Fe), moisture content, and auxiliary material (cold feed, flux) composition; Real-time sensor data: real-time feed rate. Oxygen-enriched air flow rate and oxygen concentration, fuel (natural gas / heavy oil) injection rate, online analysis data of flue gas at the furnace top (SO2%, O2%), and melt temperature (infrared thermometry / occasional measurement).

[0066] Soft measurement technology: Using a sulfur-to-gas phase distribution model (as described in formula (1,2)) and real-time operating parameters, the amount of flue gas generated at the current moment can be estimated in real time. This addresses the measurement delay issue caused by large hysteresis. Feedforward control of feedforward ingredients: When fluctuations in the sulfur content of the feed material are detected, the MPC controller pre-calculates the oxygen enrichment rate required to maintain thermal balance. And changes in fuel heat compensation. Application stage: Applied to the batching and blast control stages of the smelting reaction.

[0067] The cascade waste heat recovery unit is responsible for converting high-temperature, nonlinear flue gas heat source into a high-quality, dispatchable steam heat source. Physical composition: The waste heat boiler consists of a radiant section (membrane wall) and a convection section (tube bundle). The key feature is the addition of a dynamic thermal regulating valve group (including feedwater regulating valve, steam drum pressure regulating valve, and desuperheating water regulating valve) and a variable frequency circulating pump.

[0068] Data input: Flue gas inlet temperature flue gas flow rate Steam drum water level, main steam pressure Superheater outlet temperature. Unsteady-state heat transfer prediction: Based on the flue gas sensible heat calculation model (Equation (5)), predict the future... N Flue gas heat load impact within minutes. Multivariable decoupling control: Resolves the coupling interference between the steam drum water level (false water level) and steam pressure, using the MPC algorithm to coordinate feedwater and exhaust steam flow. Application: Applied to the flue gas cooling and steam generation process in the 1100℃~350℃ range. Prevents boiler dry-out or overfilling accidents; achieves tiered quantitative extraction of heat energy - maximizing the production of high-pressure steam for power generation in the high-temperature range (900~1100℃), and producing medium-pressure steam for process use in the medium-temperature range (600~900℃).

[0069] The reaction-heat exchange coupled acid production unit is the core of chemical energy recovery and pollutant removal, emphasizing the immediate recovery of exothermic reactions. Physical components include: a multi-stage contact converter (with an embedded high-efficiency heat exchanger), an intermediate absorption tower, and a final absorption tower. A key feature is the installation of a high-response electric regulating valve on the cooling medium (air or water) pipeline of the heat exchanger.

[0070] Data input: Inlet / outlet temperatures of each section of the converter SO2 / SO3 concentrations at each inlet / outlet, circulating acid temperature and concentration, and cooling medium flow rate. Optimization of reaction kinetics: A built-in SO2 oxidation kinetic model (Equation (8)) is used to calculate the optimal reaction path and theoretical equilibrium temperature rise under the current gas composition in real time. Adaptive temperature control: Based on the predicted change in inlet SO2 concentration, the cooling intensity of the interlayer heat exchanger is adjusted in advance, and the bypass air volume or cooling water flow rate is changed to force the bed temperature to always run along the optimal activity curve. Application: catalytic oxidation and The absorption process.

[0071] The plant's energy interconnection network unit is responsible for peak shaving and valley filling, resolving the mismatch between heat energy generation and consumption in time and space. Physical components include: a high-pressure steam header (connected to the steam turbine), a medium-pressure steam network (connected to heat tracing / drying), a low-temperature hot water network (connected to heating / domestic water), and an intelligent flow distribution station.

[0072] Data Input: Real-time energy demand, pipeline pressure distribution, and heat accumulator status of all workshops in the plant (such as electrolysis, mineral processing, and office areas). Global Scheduling Optimization: Optimizing the heat energy produced by the acid production system. Matching with overall plant demand. When predicted steam production exceeds demand, automatically increasing power generation load or charging the thermal accumulator; conversely, activating auxiliary heat sources. Application stages: heat energy transmission, distribution, and final utilization; eliminating the waste of directly depressurizing and releasing high-pressure steam, maximizing overall plant energy efficiency.

[0073] The energy-matter co-conversion control center is deployed in a high-performance industrial server. Software architecture: Data layer: OPC UA / Modbus communication interface to collect data from the underlying PLC / DCS; Model layer: Run the sulfur element dynamic balance model (formulas (1)-(3)) and the energy / exercise cascade utilization model (formulas (4)-(7)) to construct a digital twin. Algorithm layer: Integrate the model predictive control (MPC) solver and the multi-objective genetic algorithm (NSGA-II). Specific implementation logic: State perception: Calculate the current sulfur utilization rate in real time. and energy efficiency Synergistic index Rolling optimization: to maximize the objective function. With the goal of meeting environmental emission standards Under the premise of equipment safety constraints, solve for the future Optimal control vector at time 1 This includes oxygen enrichment rate, steam pressure setpoint, and heat exchange bypass opening. Closed-loop execution: The optimized... The signal is converted into a 4-20mA or bus signal and sent to the actuators of each unit. Application: Real-time monitoring, decision-making, and command issuance throughout the entire process. Result: Achieves dual closed-loop control of material and energy flow, transforming complex multivariate coupling problems into real-time mathematical optimization problems, ensuring the system always automatically cruises under globally optimal operating conditions.

[0074] This embodiment achieves a breakthrough in solving the problems of strong coupling of multiple variables and large time lag by constructing a predictive control architecture that deeply integrates physical and informational aspects. Its significant technical advantages and beneficial effects are mainly reflected in the following five dimensions: First, in existing technologies, smelting, waste heat recovery, and acid production are typically controlled as independent units, leading to a severe mismatch between the spatiotemporal distribution of material flow (sulfur resources) and energy flow (thermal energy). This invention pioneers a unified energy-material balance model across units, treating the entire process as an indivisible thermodynamic control system. By introducing a synergistic index Ψ as a navigation indicator for global optimization, this invention successfully resolves the potential conflict between pursuing high sulfur recovery rates and high thermal energy recovery rates under specific operating conditions. The system can find the optimal balance between the two objectives based on real-time calculations, fundamentally overcoming the trade-offs inherent in traditional single-objective control and achieving simultaneous maximization of resource and energy utilization efficiency.

[0075] Secondly, traditional PID feedback control often suffers from severe lag due to the inherent discontinuous feeding and drastic reaction fluctuations in the molten pool smelting process, making it difficult to avoid operational oscillations in the acid production system. This invention employs a model predictive control (MPC) strategy, achieving a shift from passive feedback to active feedforward control paradigms. Utilizing soft sensing and dynamic mechanism models, the system can anticipate load changes in the upstream smelting furnace and pre-adjust the cooling intensity of the downstream converter and the operating parameters of the waste heat boiler. This advance control mechanism effectively mitigates the impact of flue gas flow and concentration fluctuations on the catalytic reaction bed, ensuring the acid production system always operates under ideal conditions of stable airflow and balanced heat load, significantly improving the overall process's anti-interference capability and operational stability.

[0076] Third, in traditional processes, the lack of precise dynamic control over the catalyst bed temperature often forces the catalyst to operate outside its optimal activity range, limiting conversion efficiency. This invention, through precise coupling control of reaction and heat exchange, creates a highly matched thermodynamic environment for the sulfur dioxide oxidation reaction. By real-time rolling optimization of the inlet temperature and interlayer cooling rate of each section of the converter, the system can force the reaction path to closely follow the catalyst's optimal activity temperature curve. This not only maximizes the conversion depth of sulfur from the feed gas phase to the sulfuric acid liquid phase and significantly reduces the amount of unreacted sulfur dioxide remaining in the tail gas, but also effectively avoids the risk of catalyst thermal aging or sintering deactivation due to localized overheating. Thus, while ensuring compliance with environmental emission standards, it also improves the yield and quality stability of the acid product.

[0077] Fourth, addressing the common problems of high-energy underutilization and waste of waste heat resources in traditional processes, this invention establishes a full-process energy cascade utilization network that strictly adheres to the thermodynamic principles of temperature matching and cascade utilization. Based on the second law of thermodynamics (exergy balance), the system dynamically plans the flow direction of sensible heat from high-temperature flue gas and heat from chemical reactions. Through intelligent scheduling, high-grade heat energy is preferentially converted into high-pressure steam for power generation, medium-grade heat energy is used for process heat tracing, and low-grade heat energy is used for preheating or heating. This refined energy management model effectively avoids the waste of high-quality heat sources, significantly improves the system's ability to output effective work and heat, and reduces the plant's dependence on external primary energy sources.

[0078] Fifth, waste heat boilers and heat exchange equipment in smelting acid production systems face the dual threats of high-temperature thermal stress fatigue and low-temperature acid dew point corrosion. This invention explicitly incorporates equipment safety constraints into the penalty term of a multi-objective optimization function, proactively protecting the equipment's operating status. Through strict control of key parameters such as wall temperature and flue gas temperature, the system effectively prevents the heat exchange surface temperature from falling below the acid dew point, fundamentally curbing low-temperature corrosion. Simultaneously, the stable temperature control strategy significantly mitigates the alternating thermal stress caused by drastic temperature fluctuations, thereby reducing the risk of weld cracking and pipeline leakage. This not only extends the maintenance cycle and service life of core equipment but also significantly reduces the company's operating costs and unplanned downtime losses.

[0079] like Figure 9 As shown, this embodiment provides an example of an electronic device, which includes a processor and a memory coupled to the processor.

[0080] The memory stores program instructions for implementing the energy-matter co-conversion method of flue gas SO2 acid production process in any of the above embodiments.

[0081] The processor is used to execute program instructions stored in memory to perform energy-material co-conversion in the flue gas SO2 acid production process.

[0082] The processor can also be called a CPU (Central Processing Unit). A processor may be an integrated circuit chip with signal processing capabilities. A processor can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0083] Furthermore, Figure 10 This is a schematic diagram of the structure of a storage medium according to an embodiment of this application. The storage medium of this embodiment stores program instructions capable of implementing all the above-described methods. These program instructions can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0084] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0085] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

[0086] The specific embodiments of the invention have been described in detail above, but these are merely examples, and the invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this invention. Therefore, all equivalent transformations, modifications, and improvements made without departing from the spirit and principles of this invention should be included within the scope of this invention.

Claims

1. A method for energy-matter co-conversion in a flue gas SO2 acid production process, characterized in that, The energy-matter co-conversion method for the flue gas SO2 acid production process includes: The entire process equipment is defined as a unified thermodynamic control body, and a dynamic balance model of sulfur element throughout its life cycle and an energy-exertion cascade utilization model are established. Key parameters are collected in real time through belt scales, grade analysis, flue gas analyzers and thermocouples to achieve accurate distribution and tracking of sulfur in the slag phase, matte phase, flue dust phase and gas phase, and to complete the digital mapping of flue gas sensible heat and chemical reaction heat. The design incorporates an MPC architecture that includes a state observer and a predictive controller, selecting coupled variables from the smelting and acid production sides. It incorporates a sulfur dioxide oxidation reaction kinetic model and a pseudo-one-dimensional plug flow reactor model, using the current temperature and partial pressure to predict the bed outlet conversion rate and temperature rise, and feedforward compensation to respond in advance to upstream load fluctuations. An energy-matter synergy index is defined and embedded as a reward term in the MPC objective function. Under the constraints of environmental emissions, catalyst temperature window and equipment safety, the optimal control law is solved by rolling time-domain optimization to balance the competitive relationship between sulfur recovery rate and thermal energy cascade utilization rate. Based on the MPC prediction results, the waste heat recovery grade is dynamically adjusted through the plant's energy interconnection network, and the underlying actuators receive DCS / PLC instructions to achieve closed-loop control; at the same time, a decoupling strategy is used to coordinate the interference between the steam drum water level and steam pressure.

2. The energy-matter co-conversion method for flue gas SO2 acid production according to claim 1, characterized in that, The molten pool smelting furnace, waste heat boiler, flue gas purification system, contact converter, absorption tower, and plant thermal network are collectively defined as a unified generalized thermodynamic control system. The molten pool smelting furnace inlet is used as the system input boundary, and the finished acid outlet, chimney exhaust outlet, and heat network interface are used as the output boundary. A dynamic balance model of the entire life cycle of sulfur and an energy-exertive utilization model based on the first and second laws of thermodynamics are established. Key parameters such as the amount of material entering the furnace, sulfur-oxygen ratio, oxygen enrichment rate, and molten pool temperature are acquired in real time through belt scales, online / offline grade analysis, online flue gas analyzer, and thermocouple array. This enables precise distribution and tracking of sulfur in the slag phase, matte / crude metal phase, flue dust phase, and gas phase. Simultaneously, the sensible heat of flue gas and the heat of chemical reaction are digitally mapped, providing a high-fidelity state-space description for collaborative control.

3. The energy-matter co-conversion method for flue gas SO2 acid production according to claim 2, characterized in that, The process for accurately tracking the distribution of sulfur in the slag phase, matte / crude metal phase, flue dust phase, and gas phase includes the following steps: The amount of material fed into the furnace is continuously weighed by a belt scale, and the sulfur content and sulfur-oxygen ratio in the material fed into the furnace are determined based on online / offline grade analysis data to obtain the total sulfur content and sulfur-oxygen ratio parameters. The measurement results of slag quantity and matte / crude metal quantity, combined with online / offline grade analysis, yield the sulfur content entering the slag and the sulfur content entering the matte / crude metal phase; at the same time, based on the dust collection volume of the dust collection system and the sulfur content of the flue dust, the sulfur content carried by the flue dust is determined, forming the sulfur distribution in the slag phase, matte phase and flue dust phase. The sulfur distribution in the slag phase, matte phase, and flue phase, along with the oxygen enrichment rate measured by the flow meter and the molten pool temperature measured by the thermocouple, are input into the nonlinear coupled prediction model of the gas phase sulfur flow rate to calculate the amount of sulfur entering the gas phase, thus completing the complete distribution tracking of sulfur elements in the slag phase, matte / crude metal phase, flue phase, and gas phase.

4. The energy-matter co-conversion method for flue gas SO2 acid production according to claim 1, characterized in that, The design incorporates a multivariable model predictive control (MPC) system that couples a state observer and a predictive controller. The system selects smelting-side operating parameters and acid-producing-side operating parameters as coupled control variables. Based on the rolling time-domain optimization principle, the optimal control law is solved by minimizing the objective function. The predicted output vector includes the chimney sulfur dioxide concentration, finished acid concentration, total steam output, and key equipment wall temperature. An internal sulfur dioxide oxidation reaction kinetic model is incorporated, using the current temperature and gas partial pressure to predict the bed outlet conversion rate and temperature rise. A feedforward compensation mechanism is used to respond in advance to upstream smelting load fluctuations. The smelting side operating parameters include oxygen enrichment rate, feed rate, and fuel supplementation heat, while the acid production side operating parameters include the quench air volume / cooling water volume of each section of the converter, circulating acid temperature, and boiler feed water volume.

5. The energy-matter co-conversion method for flue gas SO2 acid production according to claim 4, characterized in that, The process of predicting future conversion rates and identifying time-varying parameters that consider catalyst activity decay in temperature rise using current temperature and partial pressure includes the following steps: The catalyst bed is discretized into multiple micro-volumes along the gas flow direction, with each micro-volume considered as a uniform reaction zone. Starting from the bed inlet, the instantaneous reaction rate of sulfur dioxide is calculated based on the gas temperature and partial pressure of each component within the current micro-volume. This reaction rate is divided by the sulfur dioxide inlet molar flow rate and then multiplied by the micro-volume volume to obtain the increment of the conversion rate within the micro-volume. The inlet conversion rate and the increments of each micro-volume are successively accumulated. After recursively extrapolating through all micro-volumes, the predicted value of the sulfur dioxide conversion rate at the bed outlet and the corresponding reaction temperature rise are finally obtained. At each sampling time, the measured value of sulfur dioxide conversion rate at the converter outlet is extracted and compared with the predicted value obtained by integral calculation to form the prediction error. The prediction error is multiplied by the gain coefficient determined by the covariance matrix of the previous time step and the current sensitivity vector to correct the estimated value of the activity decay factor of the previous time step, and the updated value of the activity decay factor of the current time step is obtained. At the same time, the covariance matrix is ​​updated using the same gain and sensitivity vector, and a forgetting factor is introduced to enhance the tracking ability of time-varying characteristics. The output activity decay factor is used to adjust the catalyst pre-exponential factor in real time to complete the online identification and correction of model parameters. The product of the activity decay factor and the initial pre-exponential factor forms the updated time-varying pre-exponential factor parameter, which is then substituted into the reaction rate formula to replace the original fixed pre-exponential factor, thus completing the online identification and correction of catalyst activity decay by the MPC prediction model.

6. The energy-matter co-conversion method for flue gas SO2 acid production according to claim 1, characterized in that, The process of defining and dynamically adaptively adjusting the multi-objective weights in the energy-matter synergistic index in real time includes the following steps: Real-time acquisition of three types of external signals: sulfur dioxide concentration in chimney exhaust gas, carbon emission intensity per unit product, and real-time electricity price; normalization processing is performed on each type of signal. The three normalized coefficients are input into the fuzzy logic regulator; the fuzzy reasoning process includes input membership degree calculation, rule matching and weight synthesis, centroid method defuzzification, and outputs the sulfur utilization rate weight factor, energy efficiency weight factor and collaborative reward coefficient at the current moment; The three obtained parameters replace the corresponding fixed parameters in the original objective function to form a temporary objective function that reflects the current environmental protection-economic synergy requirements. Under the premise of meeting the hard constraints of environmental emissions, the catalyst activity temperature window, and the equipment safety limits, the MPC rolling optimization solver solves the optimal control increment vector with the objective function as the goal. The optimization results are sent down to the underlying actuators to realize closed-loop optimal control driven by environmental protection-economic synergy. The three steps are repeated at the next sampling time to form a real-time adaptive loop.

7. The energy-matter co-conversion method for flue gas SO2 acid production according to claim 1, characterized in that, Using the normal operating low value of sulfur dioxide concentration in exhaust gas as the lower limit and the environmental emission limit as the upper limit, the current measured concentration is mapped to the relative position within the [low, high] range to obtain the environmental pressure coefficient. Similarly, by setting upper and lower limits for carbon emission intensity based on corporate carbon emission reduction targets, a carbon emission reduction urgency coefficient can be obtained; and by mapping the range of electricity price fluctuations, an economic incentive coefficient can be obtained.

8. The energy-matter co-conversion method for flue gas SO2 acid production according to claim 1, characterized in that, The pre-defined fuzzy rules include: if the environmental pressure coefficient is too high or the exhaust gas emissions are close to the limit, the sulfur utilization rate weight factor will be forced to the maximum value and the energy efficiency weight factor will be forced to the minimum value, while the synergistic reward coefficient will be reduced to prioritize meeting environmental constraints; if the economic incentive coefficient is too high or the electricity price is high, the energy efficiency weight factor will be significantly increased, the sulfur utilization rate weight factor will be appropriately reduced, and the synergistic reward coefficient will be increased to guide the system to generate more electricity and recover more heat energy; if the carbon emission reduction urgency coefficient is too high, the energy efficiency weight and the synergistic reward coefficient will be increased while ensuring environmental compliance; if all three are in the middle range, a balanced mode will be adopted, with each weight factor taking a middle value.

9. The energy-matter co-conversion method for flue gas SO2 acid production according to claim 1, characterized in that, The principle of temperature matching and tiered utilization is followed: high-grade heat energy in the 900~1100℃ flue gas section generates high-pressure steam for power generation or turbine driving; medium-grade heat energy in the 600~900℃ converter heat exchange section generates medium-pressure steam for process heating or drying; low-grade heat energy in the absorption tower uses circulating acid heat exchange for recovery and is used for boiler feedwater preheating, plant heating, or domestic hot water; the bottom-level actuators include oxygen-enriched spray gun regulating valves, feedwater regulating valves, converter cooling dampers, circulating acid cooling bypass valves, etc.; the waste heat boiler drum water level is designed to suppress false water level readings.

10. A flue gas SO2 acid production process energy-material co-conversion system, applied to the flue gas SO2 acid production process energy-material co-conversion method as described in any one of claims 1 to 9, characterized in that, The energy-material co-conversion system for the flue gas SO2 acid production process includes: The intelligent control unit at the smelting source is used to actively adjust the oxygen enrichment rate and fuel quantity based on the soft measurement model and MPC feedforward control of sulfur distribution to the gas phase, and to smooth the fluctuations of sulfur dioxide concentration and heat load in flue gas. The cascade waste heat recovery unit is used to convert the sensible heat of flue gas into high and medium pressure steam in stages through unsteady heat load prediction and multivariate decoupled control, so as to realize the quantitative extraction of heat energy according to grade. The reaction-heat exchange coupled acid production unit is used to embed the SO2 oxidation reaction kinetic model. It adopts adaptive temperature control to force the catalyst bed to run along the optimal activity curve, so as to achieve real-time synergy between reaction exothermic and heat exchange. The plant energy interconnection network unit is used to perform peak shaving and valley filling and quality-based distribution of waste heat from acid production based on the perception and intelligent scheduling of the plant's energy demand, thereby eliminating the spatial and temporal mismatch of heat energy. The energy-matter co-conversion control center is used to construct a digital twin and calculate the co-conversion index Ψ in real time. It utilizes MPC and multi-objective optimization to solve for the optimal instruction and drive the entire process of material and energy flow in a double-closed automatic cruise.

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