Multi-partition emulsification system

Abstract

The invention discloses a multi-partition emulsification system, and relates to the technical field of emulsification systems. Comprising a feeding module, and the feeding module comprises a feeding distributor and a plurality of feeding pipelines; each feeding pipeline is provided with a flow sensor and a pressure sensor, and the feeding state is monitored in real time; the feeding distributor distributes the raw materials of different pipelines to different subsequent treatment areas according to a preset algorithm; the detection and analysis module adopts detection technologies including spectral analysis, chromatographic analysis and viscosity measurement, and is used for comprehensively analyzing the liquid raw materials entering the system. According to multi-component components and physicochemical properties of liquid raw materials, comprehensive detection and analysis can be carried out through the feeding module, the liquid raw materials are accurately distributed to the most suitable emulsification area by means of the intelligent flow dividing module, and it is ensured that targeted fine emulsification treatment is obtained in the corresponding area; therefore, the problems of unstable emulsification effect and uneven product quality are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of emulsification systems, and in particular to a multi-zone emulsification system. Background Art

[0002] Emulsification technology is widely used in many fields such as chemical industry, pharmaceuticals, and food in modern industrial production. Traditional emulsification systems mainly rely on a single emulsification device and fixed process parameters, and it is difficult to meet the increasingly diverse liquid treatment requirements. With the rapid development of industrial automation and intelligent technologies, higher requirements are put forward for the accuracy, efficiency, and adaptability of emulsification systems.

[0003] In the prior art, first, there are significant differences in the composition and physical and chemical properties of different liquid raw materials. Traditional emulsification systems often adopt a unified treatment method and cannot perform personalized emulsification treatment according to the liquid characteristics, resulting in unstable emulsification effects and uneven product quality. For example, for liquids with complex compositions and sensitive to shear forces, such as certain solutions containing high concentrations of macromolecular organic substances and metal ions, it is difficult to achieve an ideal emulsification effect in traditional emulsification systems, and problems such as stratification, precipitation, or demulsification are likely to occur.

[0004] Secondly, the flow splitting mechanism of traditional emulsification systems is not intelligent enough. Most of them only perform rough flow splitting based on simple physical properties (such as viscosity) and cannot comprehensively consider the mutual relationship of various components and physical and chemical properties of the liquid and their influence on the emulsification effect. This causes some liquids to be processed in unsuitable emulsification areas, resulting in energy waste and a decline in product quality. For example, some liquids that are originally suitable for emulsification under mild conditions may be misallocated to the high-pressure emulsification area, resulting in overly large emulsion particles or unstable structures, affecting the performance and shelf life of the product.

[0005] Furthermore, the parameter control during the emulsification process is not precise enough. Traditional systems mainly rely on manual adjustment or simple automatic control loops to adjust parameters such as temperature, pressure, and stirring speed, and it is difficult to respond in real time to changes in liquid properties and achieve fine optimization control. This not only reduces production efficiency but also increases the defective rate and production cost. For example, during the emulsification process, if the temperature of the liquid fluctuates, it may affect the solubility and activity of the emulsifier, and thus affect the emulsification effect. However, traditional systems may not be able to adjust the temperature in a timely and accurate manner, resulting in unstable product quality.

[0006] After retrieval, the application solution with the Chinese patent application number CN202123436050.9 discloses an emulsification system, which is at least composed of a first kettle body, a second kettle body, a third kettle body, etc. Among them, the first kettle body is used to receive the first material and form a first stirred liquid through stirring and / or reaction. The second kettle body is used to receive the second material and form a second stirred liquid through stirring and / or reaction, and the types of the first material and the second material are different; the third kettle body is used to receive the first stirred liquid and the second stirred liquid and emulsify them to form an emulsion. The emulsification system in the above patent has the following deficiencies: it cannot be optimized and controlled, cannot perform reliable shunt processing according to the material situation, and the emulsification effect is not good. Summary of the Invention

[0007] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a multi-zone emulsification system.

[0008] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0009] A multi-zone emulsification system, comprising:

[0010] A feeding module, the feeding module includes a feeding distributor and a plurality of feeding pipelines; each feeding pipeline is equipped with a flow sensor and a pressure sensor to monitor the feeding state in real time; the feeding distributor distributes the raw materials in different pipelines to different subsequent processing areas according to a preset algorithm;

[0011] A detection and analysis module, the detection and analysis module adopts detection technologies, including spectral analysis, chromatographic analysis, and viscosity measurement, to comprehensively analyze the liquid raw materials entering the system;

[0012] An intelligent shunt module, the intelligent shunt module accurately distributes the liquid to the corresponding emulsification zone module according to the liquid characteristic data provided by the detection and analysis module, combined with the preset shunt rules and algorithms; the intelligent shunt module dynamically optimizes the shunt strategy according to the actual data during the emulsification process;

[0013] An emulsification zone module, each emulsification zone module mainly includes an emulsification tank body, a stirring device, an ultrasonic generator, and a homogenizing valve, and independently performs emulsification operations;

[0014] A discharging module, the discharging module discharges and collects the liquid after emulsification treatment from the system, and specifically includes a discharging pipeline, a valve, and a discharging storage tank.

[0015] Preferably: the feeding distributor is based on the type of liquid raw material, viscosity η i , surface tension γ i factors, through the formula f(x) = a1η i + a2γi +a3Q i +a4P i +b, where a1, a1, a1, a1 are weight coefficients, b is a correction term, Q i represents the flow rate of the i-th feed pipeline, with the unit of L / min, and P i represents the pressure of the i-th feed pipeline, with the unit of Pa.

[0016] Preferably: The detection and analysis module includes:

[0017] Component analysis unit: Using spectroscopy and chromatography techniques to accurately determine the content of each component in the liquid;

[0018] Physical property detection unit: Using a high-precision viscometer and surface tension meter to measure the viscosity η and surface tension γ of the liquid in real time. The viscosity is measured by the rotational viscometer method. According to the formula It is calculated, where K is the instrument constant, a is the shear stress, and n is the rotational speed; The surface tension measurement is based on the principle of the ring method or the plate method. By measuring relevant physical quantities and substituting them into the empirical formula to determine, where m is the mass, g is the acceleration due to gravity, and d is the diameter of the ring or plate;

[0019] Data analysis and processing unit: Integrate and analyze the data obtained by each detection unit. By establishing a mathematical model, predict the emulsification effect of the liquid under different emulsification conditions, and transmit the analysis results to the intelligent shunt module and the emulsification zone module.

[0020] Preferably: The intelligent shunt module includes:

[0021] Fuzzy logic inference unit, receiving the liquid component and physical and chemical property data transmitted by the feed module, and comprehensively analyzing and inferring the data based on the fuzzy logic algorithm; According to the pre-established fuzzy rule base; The fuzzy logic inference unit matches and infers the input data with the fuzzy rule base to preliminarily determine the possible emulsification zone number or type suitable for each liquid sample.

[0022] Preferably: The intelligent shunt module further includes:

[0023] Neural network prediction unit, using the trained neural network model to further accurately predict and classify the liquid samples; The neural network model adopts a multi-layer feedforward neural network structure; Using the liquid characteristic data provided by the feed module as the input vector of the input layer, through the forward propagation calculation of the network, obtain the prediction result of the output layer.

[0024] Preferably: The intelligent shunt module further includes:

[0025] The intelligent decision-making unit synthesizes the output results of the fuzzy logic reasoning unit and the neural network prediction unit to make a final intelligent diversion decision; according to the set decision rules and priorities, it determines the specific emulsification area to which each liquid sample should be assigned or the special treatment measures to be taken; if the results of the fuzzy logic reasoning unit and the neural network prediction unit are consistent or similar, the decision is made according to the result with a high probability; if there are differences between the two results, a comprehensive judgment is made according to the preset rules.

[0026] Preferably, the intelligent diversion module further includes:

[0027] The dynamic diversion adjustment unit continuously monitors the changes in the composition and physical and chemical properties of the liquid sample before it officially enters the emulsification module; the dynamic diversion adjustment unit conducts a comparative analysis of the real-time monitored data and the initial detection data. If it is found that the property change of the liquid sample exceeds the preset range, it promptly reassigns it to a more suitable emulsification area.

[0028] Preferably, the emulsification module includes:

[0029] The multi-zone fine emulsification unit conducts emulsification treatment on the liquid for the specific emulsification area determined by the intelligent diversion module; according to the characteristics and requirements of different emulsification areas, it applies the preset emulsification equipment and process parameters for emulsification operations;

[0030] The intelligent emulsifier addition unit calculates and controls the addition amount and addition timing of the emulsifier according to the liquid composition and physical and chemical property data provided by the feeding module and the emulsification area information determined by the intelligent diversion module.

[0031] Preferably, the emulsification module further includes:

[0032] The real-time monitoring and feedback unit, during the emulsion preparation process, continuously monitors the key indicators of the emulsion and the operating status of the emulsification equipment, and collects real-time data on the particle size distribution, viscosity change, temperature, and pressure of the emulsion; it conducts a comparative analysis of these data with the preset emulsification indicators, promptly discovers deviations, and generates feedback signals.

[0033] Preferably, the emulsification module controls the liquid temperature in the emulsification tank within the set range through a heating or cooling device. Let the temperature set value be T set , and the actual temperature be T. The PID feedback control algorithm is adopted, and its control formula is where u(t) is the control output signal, K p is the proportionality coefficient, T i is the integral time constant, T d is the differential time constant, e(t) = T set -T is the temperature deviation.

[0034] The beneficial effects of the present invention are as follows:

[0035] 1. The present invention can comprehensively detect and analyze the multi-component and physical-chemical properties of liquid raw materials through the feeding module, and with the help of the intelligent shunt module, accurately distribute them to the most suitable emulsification area to ensure targeted fine emulsification treatment in the corresponding area, thus solving the problems of unstable emulsification effect and uneven product quality.

[0036] 2. Each emulsification zone module of the present invention is equipped with an independent intelligent control system, which can accurately control the key parameters in the emulsification process; the temperature control adopts the PID feedback control algorithm to stabilize the temperature fluctuation within a small range to ensure that the emulsification process is carried out under the best thermodynamic conditions; the accurate setting and control of time can ensure that the emulsification reaction is sufficient and not excessive, avoiding energy waste; the accurate regulation of the emulsifier addition amount combines advanced metering pump technology and a real-time monitoring feedback mechanism to make the emulsion particles uniform, fine and stable, greatly improving the quality consistency of the product and meeting the strict quality requirements of high-end products.

[0037] 3. During the emulsification process, the system of the present invention can collect and analyze various data information in real time, such as changes in the composition of the liquid, fluctuations in physical and chemical properties, and the operating status of the equipment. According to these dynamic information, it can automatically adjust the emulsification strategy and parameters in a timely manner to achieve the adaptive operation of the system, optimize the emulsification process, ensure that the production process always remains stable and reliable, effectively reduce the risk of production accidents caused by human factors or external interference, and improve the continuity and stability of production. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a framework diagram of a multi-zone emulsification system proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The technical solutions of the present invention will be further described in detail below in conjunction with the specific embodiments.

[0040] Example 1:

[0041] A multi-zone emulsification system includes:

[0042] A feeding module, the feeding module includes a feeding distributor and a plurality of feeding pipelines; each feeding pipeline is equipped with a flow sensor (Q i represents the flow rate of the i-th feeding pipeline, unit: L / min) and a pressure sensor (P i represents the pressure of the i-th feeding pipeline, unit: Pa), which are used to monitor the feeding status in real time; the feeding distributor is based on a preset algorithm (such as based on the type of liquid raw material, viscosity η i , surface tension γ i factors, through the formula f(x)=a1ηi +a2γ i +a3Q i +a4P i +b, where a1, a1, a1, a1 are weighting coefficients and b is a correction term) distributes the raw materials from different pipelines to different subsequent processing areas to ensure that the raw materials can be reasonably diverted according to their characteristics;

[0043] The detection and analysis module uses detection technologies, including spectral analysis, chromatographic analysis, viscosity measurement, etc., to comprehensively analyze the liquid raw materials entering the system;

[0044] The intelligent diversion module distributes the liquid accurately to the corresponding emulsification zone modules according to the liquid characteristic data provided by the detection and analysis module, combined with the preset diversion rules and algorithms; the intelligent diversion module dynamically optimizes the diversion strategy according to the actual data during the emulsification process;

[0045] Each emulsification zone module mainly includes core components such as an emulsification tank body, a stirring device, an ultrasonic generator, and a homogenizing valve, and conducts emulsification operations independently;

[0046] The discharging module discharges and collects the liquid after emulsification treatment from the system, specifically including a discharging pipeline, a valve, and a discharging storage tank.

[0047] Among them, the detection and analysis module includes:

[0048] The component analysis unit: uses spectral analysis and chromatographic analysis technologies to accurately measure the content of each component in the liquid (assuming the mass fraction of a certain key component in the liquid is w j , and obtains its quantitative relationship with the spectral or chromatographic signal intensity I through a specific spectral or chromatographic analysis method, such as w j =k1I j +k2, where k1 and k2 are calibration coefficients); j +k2, where k1, k2 are calibration coefficients);

[0049] The physical property detection unit: uses a high-precision viscometer and surface tensiometer to measure the viscosity η and surface tension γ of the liquid in real time. The viscosity measurement uses the rotational viscometer method and is calculated according to the formula (where K is the instrument constant, a is the shear stress, and n is the rotational speed); the surface tension measurement is based on the principle of the ring method or the plate method, and is determined by measuring relevant physical quantities and substituting them into the empirical formula (where m is the mass, g is the acceleration due to gravity, and d is the diameter of the ring or plate);

[0050] Data analysis and processing unit: Integrates and analyzes the data obtained by each detection unit. By establishing a mathematical model (such as the multiple linear regression model y = β0 + β1x1 + β2x2 + … + β n x n + ∈, where y is the target variable, x1, x2, …, x n are independent variables, β0, β1, …, β n are regression coefficients, and ∈ is the error term), predicts the emulsification effect of the liquid under different emulsification conditions, and transmits the analysis results to the intelligent shunt module and the emulsification zone module to provide a basis for subsequent intelligent decision-making.

[0051] Among them, the intelligent shunt module includes:

[0052] Fuzzy logic reasoning unit, which receives the liquid composition and physical and chemical property data transmitted by the feeding module, and comprehensively analyzes and reasons the data based on the fuzzy logic algorithm; according to the pre-established fuzzy rule base, the fuzzy rule base contains the fuzzy relationships between various liquid compositions and physical and chemical properties and the optimal emulsification region. For example, a specific composition combination and viscosity range correspond to the liquid category suitable for the high-pressure emulsification region; the fuzzy logic reasoning unit matches and reasons the input data with the fuzzy rule base to preliminarily determine the possible emulsification region number or type (such as Region 1 - high-pressure emulsification zone, Region 2 - medium-pressure emulsification zone, Region 3 - low-pressure emulsification zone, etc.) for each liquid sample;

[0053] Neural network prediction unit, which uses the trained neural network model to further accurately predict and classify the liquid samples; the neural network model adopts a multi-layer feedforward neural network structure, with multiple hidden layers and a large number of neuron nodes; takes the liquid characteristic data (such as composition content, viscosity, density, surface tension, etc.) provided by the feeding module as the input vector of the input layer, and through the forward propagation calculation of the network, obtains the prediction result of the output layer, that is, the specific emulsification region category to which each liquid sample belongs (such as accurate to the specific emulsification region number or directly output the optimal process parameter range of this region); the neural network model automatically learns and identifies complex patterns and rules on the basis of training with a large amount of known liquid sample data, so as to achieve high-precision prediction of unknown liquid samples;

[0054] Intelligent decision-making unit, which synthesizes the output results of the fuzzy logic reasoning unit and the neural network prediction unit to make a final intelligent shunt decision; determines the specific emulsification region to which each liquid sample should be assigned or the special treatment measures to be taken according to the set decision rules and priorities; for example, if the results of the fuzzy logic reasoning unit and the neural network prediction unit are the same or similar, the decision is made according to the high-probability result; if there are differences between the two results, a comprehensive judgment is made according to the preset rules (such as giving priority to the neural network prediction result or weighing according to the empirical rules).

[0055] The dynamic diversion adjustment unit continuously monitors the changes in the composition and physical and chemical properties of the liquid sample before it officially enters the emulsification module. Since the liquid may be affected by external factors during storage or transportation (such as temperature changes causing volatilization or crystallization of components, chemical reactions with other substances, etc.), its properties may change to a certain extent. The dynamic diversion adjustment unit compares and analyzes the real-time monitoring data with the initial detection data. If it is found that the changes in the properties of the liquid sample exceed the preset range, it will be promptly reallocated to a more suitable emulsification area or other special processing flow.

[0056] Wherein, the emulsification module comprises:

[0057] The multi-zone fine emulsification unit emulsifies the liquid in the specific emulsification area determined by the intelligent diversion module; according to the characteristics and requirements of different emulsification areas, the preset emulsification equipment and process parameters are used for emulsification operations. For example, in the high-pressure emulsification area, a high-pressure homogenizer is used to strongly shear, impact and cavitate the liquid, so that the components in the liquid are fully broken and mixed to form tiny droplets; in the medium-pressure emulsification area, ultrasonic emulsification equipment is used in combination with a specific emulsifier addition process to promote the emulsification of the liquid through the cavitation effect of ultrasonic waves and mechanical stirring; in the low-pressure emulsification area, gentle stirring and heating methods are used, combined with special surfactant treatment, to achieve stable emulsification of the liquid;

[0058] The emulsifier intelligent addition unit calculates and controls the amount and timing of emulsifier addition based on the liquid composition and physical and chemical properties data provided by the feed module and the emulsification area information determined by the intelligent diversion module. Then, the emulsifier is accurately added to the liquid through a high-precision metering pump or injection device to ensure that the emulsifier is fully mixed with the liquid and exerts the best effect;

[0059] The real-time monitoring and feedback unit monitors the key indicators of the emulsion and the operating status of the emulsification equipment in real time during the emulsion preparation process. Through various sensors installed on the emulsification equipment (such as particle size sensors, viscosity sensors, temperature sensors, pressure sensors, etc.), real-time data such as particle size distribution, viscosity changes, temperature and pressure of the emulsion are collected. These data are compared and analyzed with the preset emulsification indicators to detect deviations in time and generate feedback signals.

[0060] The emulsification module controls the liquid temperature in the emulsification tank within a set range (assuming the temperature setting value is T) by means of a heating or cooling device (such as an electric heating wire or a chiller). set , the actual temperature is T), using PID feedback control algorithm, the control formula is: Where u(t) is the control output signal, Kp is the proportionality coefficient, T i is the integral time constant, T d is the derivative time constant, e(t) = T set -T is the temperature deviation, ensuring that the emulsification process is carried out under suitable temperature conditions to improve the emulsification effect and product quality.

[0061] As described above, it is only the preferred specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, should be covered within the protection scope of the present invention.

Claims

1. A multi-zone emulsification system, characterized in that, Including: A feeding module, which includes a feeding distributor and multiple feeding pipelines; each feeding pipeline is equipped with a flow sensor and a pressure sensor to monitor the feeding status in real time; The feeding distributor distributes raw materials in different pipelines to different subsequent processing areas according to a preset algorithm; A detection and analysis module, which uses detection technologies including spectral analysis, chromatographic analysis, and viscosity measurement to comprehensively analyze the liquid raw materials entering the system; An intelligent shunt module, which accurately distributes the liquid to the corresponding emulsification partition module according to the liquid characteristic data provided by the detection and analysis module, combined with preset shunt rules and algorithms; the intelligent shunt module dynamically optimizes the shunt strategy according to the actual data during the emulsification process; An emulsification partition module, each of which mainly includes an emulsification tank body, a stirring device, an ultrasonic generator, and a homogenizing valve, and independently performs emulsification operations; A discharging module, which discharges and collects the liquid after emulsification treatment from the system, specifically including a discharging pipeline, a valve, and a discharging storage tank.

2. The multi-zone emulsification system according to claim 1, wherein, The feed distributor is based on the type and viscosity η of the liquid raw material i , surface tension γ i factor, through the formula f(x) = a1η i + a2γ i + a3Q i + a4P i + b, where a1, a1, a1, a1 are weight coefficients, b is a correction term, Q i represents the flow rate of the i-th feed pipe, in L / min, and P i represents the pressure of the i-th feed pipe, in Pa.

3. The multi-zone emulsification system according to claim 1, wherein, The said detection and analysis module includes: A component analysis unit: using spectral analysis and chromatographic analysis technologies to accurately determine the content of each component in the liquid; Physical property detection unit: Using a high-precision viscometer and surface tensiometer to measure the viscosity η and surface tension γ of the liquid in real time. The viscosity is measured by the rotational viscometer method and calculated according to the formula where K is the instrument constant, a is the shear stress, and n is the rotational speed; the surface tension is measured based on the principle of the ring method or plate method. By measuring the relevant physical quantities and substituting them into the empirical formula to determine, where m is the mass, g is the acceleration due to gravity, and d is the diameter of the ring or plate; A data analysis and processing unit: integrating and analyzing the data obtained by each detection unit, predicting the emulsification effect of the liquid under different emulsification conditions by establishing a mathematical model, and transmitting the analysis results to the intelligent shunt module and the emulsification partition module.

4. A multi-zone emulsification system according to claim 1, wherein The said intelligent shunt module includes: A fuzzy logic reasoning unit, which receives the liquid component and physical and chemical property data transmitted by the feeding module, and comprehensively analyzes and reasons the data based on the fuzzy logic algorithm; according to the pre-established fuzzy rule base; the fuzzy logic reasoning unit matches and reasons the input data with the fuzzy rule base to preliminarily determine the possible suitable emulsification area number or type for each liquid sample.

5. A multi-zone emulsification system according to claim 4, wherein, The said intelligent shunt module further includes: A neural network prediction unit, which uses the trained neural network model to further accurately predict and classify the liquid samples; the neural network model adopts a multi-layer feedforward neural network structure; taking the liquid characteristic data provided by the feeding module as the input vector of the input layer, and obtaining the prediction result of the output layer through the forward propagation calculation of the network.

6. The multi-zone emulsification system according to claim 5, wherein, The said intelligent shunt module further includes: An intelligent decision-making unit, which comprehensively considers the output results of the fuzzy logic reasoning unit and the neural network prediction unit to make a final intelligent shunt decision; determining the specific emulsification area or special treatment measures that each liquid sample should be assigned to according to the set decision rules and priorities; if the results of the fuzzy logic reasoning unit and the neural network prediction unit are consistent or similar, make a decision according to the result with a high probability; if there are differences between the two results, make a comprehensive judgment according to the preset rules.

7. The multi-zone emulsification system according to claim 6, characterized in that, The said intelligent shunt module further includes: The dynamic flow splitting and adjustment unit continuously monitors the changes in the composition and physical and chemical properties of the liquid sample before it officially enters the emulsification module; the dynamic flow splitting and adjustment unit conducts a comparative analysis of the real-time monitored data with the initial detection data, and if it is found that the property change of the liquid sample exceeds the preset range, it promptly reallocates it to a more suitable emulsification area.

8. The multi-zone emulsification system according to claim 1, characterized in that, The emulsification module includes: The multi-zone fine emulsification unit emulsifies the liquid for the specific emulsification area determined by the intelligent flow splitting module; according to the characteristics and requirements of different emulsification areas, it performs emulsification operations using preset emulsification equipment and process parameters. The intelligent emulsifier addition unit calculates and controls the addition amount and addition timing of the emulsifier based on the liquid composition and physical and chemical property data provided by the feeding module and the emulsification area information determined by the intelligent flow splitting module.

9. The multi-zone emulsification system according to claim 8, wherein, The emulsification module further includes: The real-time monitoring and feedback unit, during the emulsion preparation process, continuously monitors the key indicators of the emulsion and the operating status of the emulsification equipment, collects real-time data on the particle size distribution, viscosity change, temperature, and pressure of the emulsion; conducts a comparative analysis of these data with the preset emulsification indicators, promptly discovers deviations, and generates feedback signals.

10. The multi-zone emulsification system according to claim 9, wherein, The emulsification module controls the temperature of the liquid in the emulsification tank within a set range through a heating or cooling device. Let the temperature set value be T set , and the actual temperature be T. The PID feedback control algorithm is adopted, and its control formula is where u(t) is the control output signal, K p is the proportionality coefficient, T i is the integral time constant, T d is the derivative time constant, and e(t) = T set -T is the temperature deviation.

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