Cooling-solventing-out coupling crystallization simulation optimization method and system based on Aspen Plus V14 and MATLAB cooperation

Abstract

The invention relates to a cooling-solvating-out coupling crystallization simulation optimization method and a cooling-solvating-out coupling crystallization simulation optimization system based on Aspen Plus V14 and MATLAB coordination. According to the method, the continuous adding process of the solvating-out agent is dispersed into 80-120 batches of operation, a double-variable dynamic solubility model is combined, solvent composition influences are described through a CNIBS / RK model, temperature dependence is represented through an Apelblat equation, and model parameters are optimized through an lsqnonlin function of MATLAB. And then real-time data interaction between the Aspen Plus V14 and the MATLAB is realized through a COM (Component Object Model) technology and an ActiveX automatic server. According to the invention, high-precision simulation of solvating-out crystallization is realized, and the error of obtained granularity data is more than 10% compared with that of an experiment; meanwhile, the method and the system provided by the invention also provide reference for efficient simulation of the coupling crystallization process.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical crystallization, and particularly relates to a simulation optimization method and system for cooling-antisolvent coupled crystallization based on the cooperation of Aspen Plus V14 and MATLAB. Background Art

[0002] Azelaic Acid, as an important fine chemical, is widely used in the fields of medicine, cosmetics and polymer materials. It exists in multiple crystal forms (such as α crystal form, β crystal form), and the physical and chemical properties of different crystal forms are significantly different. The α crystal form has become the preferred target for industrial production due to its higher solubility. However, in traditional crystallization processes, the control of crystal forms has not been emphasized, resulting in a mixture of the two crystal forms being mainly available on the market.

[0003] In the prior art, a cooling-antisolvent coupled crystallization method was developed in "A Preparation Method of Azelaic Acid alpha Crystal Form (CN114751818A)", that is, by adding an antisolvent (water) to change the solvent polarity, the α crystal form of azelaic acid can be stably prepared. However, in the process of transitioning from laboratory to industrial production, if large-scale experiments are directly carried out, not only the cost is high, but also once problems occur, the adjustment difficulty will be very large, resulting in huge waste of resources. Therefore, the simulation of the process is very necessary and important. Due to the complexity of the crystallization process itself, it is difficult to achieve high-precision chemical simulation, which further increases the difficulty from laboratory to industrial production. Currently, the simulation of crystallization mainly focuses on molecular dynamics simulation based on MS software (CN119207596A), and less attention is paid to the whole process of crystallization. Some research has simulated the ammonium sulfate crystallization process using the crystallization module that appeared in Aspen Plus V10 in 2017, but many problems have emerged in practical applications. In terms of accuracy, when this module predicts the particle size distribution of ammonium sulfate crystals, the error often exceeds 20% compared with the data collected at the industrial production site. After years of development, the crystallization module in Aspen Plus V14 has been improved many times, and its accuracy has been greatly improved, but there are still limitations, such as it can only simulate cooling and evaporation crystallization processes, and it is still unable to directly simulate the commonly used antisolvent crystallization operation.

[0004] Therefore, there is an urgent need to develop a system and method that integrates software control, dynamic model optimization and software cooperation to improve the accuracy of data simulation, accurately describe various crystallization processes, and provide guidance for the industrial scale-up of crystallization operations. Summary of the Invention

[0005] In view of the problem that the existing cooling-dissolution coupled crystallization process cannot be accurately simulated, the present invention provides a cooling-dissolution coupled crystallization simulation optimization method and system based on the collaboration of Aspen Plus V14 and MATLAB, which can solve the technical problem that AspenPlus V14 cannot handle the dynamic changes of solvent composition, and significantly improve the simulation accuracy and process control capability of the dissolution crystallization process.

[0006] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0007] A cooling-dissolution coupled crystallization simulation optimization method based on the collaboration of Aspen Plus V14 and MATLAB specifically comprises the following steps:

[0008] S1. A process flow of a cooling crystallizer and a dissolution crystallizer in series was constructed in Aspen Plus V14, wherein the cooling crystallizer was configured to generate a supersaturated solution by programmed cooling, and the dissolution crystallizer was configured to add a dissolution agent in batches to dynamically control the solvent composition;

[0009] S2. Construct a dynamic solubility model based on MATLAB, including a bivariate solubility model based on solvent composition and temperature, and fit the Apelblat model parameters using nonlinear least squares method;

[0010] S3. Use COM technology and ActiveX automation server to establish a real-time data interaction interface between Aspen Plus V14 and MATLAB, so that Aspen Plus V14 can output the solvent composition, temperature, supersaturation and other data of the dissolution crystallizer to MATLAB. MATLAB will return the fitted Apelblat model parameters to Aspen Plus V14 to update the dynamic solubility model of the dissolution crystallizer.

[0011] S4. During the process of adding the solvent, step S3 is cyclically executed until the addition of the solvent is completed or a preset termination condition is reached.

[0012] Preferably, the adding of solvent water in batches comprises:

[0013] Discretize the dissolution agent addition process into 80-150 batches;

[0014] The amount of solvent added in a single batch is 0.8%-1.25% of the total amount added.

[0015] Preferably, in MATLAB, the triggering conditions for adding the solvent in batches (both of which are satisfied at the same time) are:

[0016] The temperature of the dissolution crystallizer is ≤10℃;

[0017] After each batch of solvent is added, the molar fraction of solvent water in the solution is ≤50%.

[0018] Preferably, the dynamic solubility model comprises:

[0019] The effect of solvent composition on solubility is calculated using the CNIBS / RK model, using the formula:

[0020] lnx=k 0 +k 1 w+k 2 w 2 +k 3 w 3 +k 4 w 4

[0021] Where x is the molar fraction solubility, w is the molar fraction of water in the solvent, and k 0 -k 4 is the fitting parameter;

[0022] The temperature dependence is described by the Apelblat model, as follows:

[0023]

[0024] Wherein, T represents temperature (K);

[0025] The Apelblat model parameters A, B, and C were fitted using the least squares method using MATLAB.

[0026] Preferably, when fitting the Apelblat model parameters, the initial parameter range is A∈[0,2], B∈[-2,0], C∈[0,2], and R 2 >0.95 is the convergence condition.

[0027] Preferably, the data transmission delay of the real-time data interaction interface is less than 1 second, and the interaction frequency is within 10 seconds after each batch of operations is completed.

[0028] The batch interval time is ≤2 minutes for industrial grade, and the volume of the dissolution crystallizer is ≥10L; the addition rate of each batch of dissolution agent is 20-100mL / min.

[0029] Preferably, the preset termination condition includes at least one of the following conditions:

[0030] The total amount of solvent added is 4 times the amount of the initial solution;

[0031] The molar fraction of water in the solution in the crystallizer is ≥50%.

[0032] The present invention also provides a simulation optimization system for the cooling-antisolvent coupled crystallization of azelaic acid α-crystal based on the cooperation of Aspen Plus V14 and MATLAB, which is used to implement a simulation optimization method for the cooling-antisolvent coupled crystallization of azelaic acid α-crystal based on the cooperation of Aspen Plus V14 and MATLAB as described above, including:

[0033] The Aspen Plus V14 module is configured for process flow modeling and batchwise antisolvent control;

[0034] The MATLAB module is configured for constructing a dynamic solubility model and optimizing parameters;

[0035] The data interaction engine realizes the real-time communication between Aspen Plus V14 and MATLAB based on COM technology and ActiveX server.

[0036] Preferably, the MATLAB module is further configured as follows:

[0037] When it is detected that the mole fraction of water in the solution ≥ 50%, the antisolvent addition rate and temperature parameters are locked. At this time, the termination condition is reached and no more antisolvent is added;

[0038] Trigger the exception handling mechanism, including parameter fallback and alarm functions. If there are particularly large temperature changes, the solid accounts for ≥ 40% of the total volume of the solution, and Aspen Plus V14 cannot converge in the calculation during the simulation, it is considered that an abnormality has occurred in these cases, and the current operation is stopped and the parameters are returned to the previous data.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. Through the strategy of batchwise addition of antisolvent and real-time dynamic solubility model update, the present invention effectively solves the problem of model inaccuracy caused by the dynamic change of solvent composition in traditional process simulation; the batchwise discretization operation (80 - 150 batches) enables Aspen Plus V14 to gradually respond to the change of solvent composition, realizes the process of continuous addition of solvent in the simulation, and significantly improves the simulation accuracy. At the same time, based on the MATLAB-based bivariate (solvent composition, temperature) solubility model (CNIBS / RK + Apelblat), the model parameters are dynamically corrected through the parameter optimization algorithm to ensure that the simulation process highly matches the dynamic behavior of the actual process.

[0041] 2. The real-time data interaction interface constructed by the present invention using COM technology and ActiveX automation server realizes the communication between Aspen Plus V14 and MATLAB. After each batch operation, key data such as solvent composition and temperature output by Aspen Plus V14 can be transmitted to MATLAB within 10 seconds for model optimization, and the updated parameters are then transmitted back to Aspen Plus V14 to form a closed-loop control. This interaction mechanism with millisecond-level delay (<1 second) supports real-time regulation and exception handling (such as timeout alarm and parameter rollback) of industrial processes, significantly improving the system accuracy.

[0042] 3. The modular design of the present invention supports the expansion from laboratory to industrial production. For example, by adjusting the batch interval time (industrial level ≤ 2 minutes) and the volume of the antisolvent crystallizer (≥ 10 L), it can adapt to different scale requirements. In addition, the bivariate solubility model and the real-time feedback mechanism can flexibly adapt to various antisolvent types (such as water and ethanol) and crystallization systems, providing a general technical framework for the optimization of complex crystallization processes, with both high efficiency and environmental friendliness.

[0043] 4. The present invention realizes high-precision simulation of antisolvent crystallization, and the obtained particle size data has an error of no more than 10% compared with the experiment. At the same time, the present invention also provides a reference for the efficient simulation of coupled crystallization processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art.

[0045] Figure 1 It is a schematic diagram of the overall process of the simulation optimization method of the present invention.

[0046] Figure 2 It is a schematic diagram of the structure of the simulation optimization system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following will more clearly and completely illustrate the present invention through a preferred embodiment in conjunction with the drawings, but the present invention is not limited to the scope of the described embodiments.

[0048] Embodiment 1

[0049] As Figure 1 shown, this embodiment is based on the simulation optimization of the cooling-antisolvent coupled crystallization of α-crystalline form of azelaic acid in cooperation with Aspen Plus V14 and MATLAB, and specifically includes the following steps:

[0050] S1. Set up a process flow in Aspen Plus V14 where a cooling crystallizer and a precipitation crystallizer are connected in series. The cooling crystallizer is configured to cool down the temperature programmatically to generate a supersaturated solution, and the precipitation crystallizer is configured to add the precipitant in batches to dynamically regulate the solvent composition. In this example, the precipitant is water. Initially, the precipitation crystallizer contains a mixture of propionic acid and azelaic acid, with propionic acid as the solvent.

[0051] In one example, set the precipitation crystallizer in Aspen Plus V14 to the batch operation mode (BatchOp module). Discretize the process of adding the precipitant water into 80 - 120 batches, with the amount added in each batch accounting for 0.8% - 1.25% of the total precipitant (for example, when the total amount of precipitant is 4000 mL, the amount added in a single batch is 34 - 50 mL). Control the addition rate of the precipitant (20 - 100 mL / min) through the "UnitProcedures" function of Aspen Plus V14. Conduct batch operations to approximate the continuous process, meet the principle of small-step integration, with a simulation error < 5%, and avoid the mixing of crystal forms caused by drastic fluctuations in supersaturation.

[0052] In a specific example, the triggering conditions for adding the precipitant in batches are that both of the following two conditions are met simultaneously: the temperature of the precipitation crystallizer ≤ 10°C; the mole fraction of water as the precipitant in the solution ≤ 50% after adding each batch of the precipitant.

[0053] S2. Build a dynamic solubility model based on MATLAB, including a bivariate solubility model based on solvent composition and temperature, and fitting the parameters of the Apelblat model using the nonlinear least squares method.

[0054] In one example, the dynamic solubility model includes:

[0055] In MATLAB, fit the parameters based on experimental data (such as solubility at different propionic acid - water mole ratios), and calculate the influence of solvent composition on solubility through the CNIBS / RK model. The formula is:

[0056] lnx = k 0 + k 1 w + k 2 w 2 + k 3 w 3 + k 4 w 4

[0057] where x is the mole fraction solubility, w is the mole fraction of water in the solvent, and k 0 - k 4 are the fitting parameters, and the solvent is water and propionic acid.

[0058] The temperature dependence is described by the Apelblat model, as follows:

[0059]

[0060] Where T represents temperature, A, B, and C are Apelblat model parameters;

[0061] The least squares method was used to fit the parameters A, B, and C using MATLAB.

[0062] The Apelblat model parameters were fitted by the lsqnonlin function of MATLAB. The initial parameter range was A∈[0,2], B∈[-2,0], C∈[0,2], and R 2 >0.95 is the convergence condition.

[0063] By constructing a two-variable dynamic solubility model, the solubility changes under the combined effects of solvent composition and temperature can be accurately predicted, with a model prediction error of less than 1%.

[0064] S3. Use COM technology and ActiveX automation server to establish a real-time data interaction interface between Aspen Plus V14 and MATLAB, so that Aspen Plus V14 can output the solvent composition, temperature, supersaturation and other data of the dissolution crystallizer to MATLAB. MATLAB will return the optimized Apelblat model parameters to Aspen Plus V14 to update the dynamic solubility model of the dissolution crystallizer.

[0065] In one embodiment, the data transmission delay of the real-time data interaction interface is less than 1 second, and the interaction frequency is completed within 10 seconds after each batch of operations. The specific configuration steps are as follows:

[0066] Call the actxserver function in MATLAB to create the COM object of Aspen Plus V14;

[0067] Read Aspen Plus V14 stream data (such as solvent composition, temperature) through the COM interface;

[0068] The optimized Apelblat model parameters are sent back to Aspen Plus to update the dissolution crystallizer module.

[0069] S4. During the process of adding the solvent, step S3 is cyclically executed until the addition of the solvent is completed or a preset termination condition is reached.

[0070] In one embodiment, the preset termination conditions include: the total amount of solvent added reaches 4 times the amount of the initial solution (the volume of propionic acid in this embodiment); the molar fraction of water in the solution is ≥50%.

[0071] Example 2

[0072] As Figure 2 shown, this embodiment provides a simulation optimization system for the cooling-antisolvent coupled crystallization of azelaic acid α-crystal based on the cooperation of Aspen Plus V14 and MATLAB, which is used to realize the simulation optimization of the cooling-antisolvent coupled crystallization of azelaic acid α-crystal based on the cooperation of Aspen Plus V14 and MATLAB, including:

[0073] The Aspen Plus V14 module is configured for process flow modeling and batchwise antisolvent control; it includes a cooling crystallizer and an antisolvent crystallizer;

[0074] The MATLAB module is configured for dynamic solubility model construction and parameter optimization;

[0075] The data interaction engine realizes real-time communication between Aspen Plus V14 and MATLAB based on COM technology and the ActiveX server;

[0076] The control feedback module is used to handle abnormal situations, including abnormal alarm and whether to terminate the addition of the antisolvent. When an abnormality occurs, it can feedback to the Aspen Plus V14 module to adjust the injection rate, etc.

[0077] In one embodiment, the MATLAB module is further configured to:

[0078] When it is detected that the mole fraction of water in the solution of the antisolvent crystallizer ≥ 50%, lock the antisolvent addition rate and temperature parameters;

[0079] Trigger an abnormal handling mechanism, including parameter rollback and alarm functions. For example, when the solubility simulation error > 5% or the MATLAB calculation times out (> 60 seconds), trigger an alarm and automatically roll back to the parameters of the previous batch.

[0080] Example 3

[0081] In this embodiment, the antisolvent addition rate is 20 mL / min, the initial temperature of the cooling crystallizer is 30 °C, and it is cooled to 10 °C at a rate of 1 °C / min to generate a supersaturated solution; the antisolvent crystallizer adds water in 80 batches (a total of 4000 mL, 50 mL per batch), and the temperature is controlled at 10 ± 0.5 °C.

[0082] Call the CNIBS / RK model in MATLAB to calculate the solubility values at different water mole fractions, and optimize the parameters in combination with the Apelblat equation.

[0083] After each batch operation, Aspen Plus V14 outputs the stream data (water mole fraction, temperature) to MATLAB, and the time taken to transmit the optimized parameters back is < 30 seconds.

[0084] In this example, the median simulated crystal particle size is 96.4 μm (experimental value 102.4 μm), and the error compared with the experimental result is 5.8%.

[0085] Example 4

[0086] In this example, the addition rate of the antisolvent is 50 mL / min, the initial temperature of the cooling crystallizer is 30 °C, and it is cooled to 10 °C at a rate of 1 °C / min to form a supersaturated solution; water (4000 mL in total, 50 mL per batch) is added to the antisolvent crystallization vessel in 80 batches, and the temperature is controlled at 10 ± 0.5 °C.

[0087] The CNIBS / RK model is called in MATLAB to calculate the solubility values at different water mole fractions, and the parameters are optimized in combination with the Apelblat equation.

[0088] After each batch operation, Aspen Plus V14 outputs the stream data (water mole fraction, temperature) to MATLAB, and the time taken to transmit the optimized parameters back is < 30 seconds.

[0089] In this example, the median simulated crystal particle size is 101.7 μm (experimental value 105.6 μm), and the error compared with the experimental result is 3.6%.

[0090] Example 5

[0091] In this example, the addition rate of the antisolvent is 100 mL / min, the initial temperature of the cooling crystallizer is 30 °C, and it is cooled to 10 °C at a rate of 1 °C / min to form a supersaturated solution; water (4000 mL in total, 50 mL per batch) is added to the antisolvent crystallization vessel in 80 batches, and the temperature is controlled at 10 ± 0.5 °C.

[0092] The CNIBS / RK model is called in MATLAB to calculate the solubility values at different water mole fractions, and the parameters are optimized in combination with the Apelblat equation.

[0093] After each batch operation, Aspen Plus V14 outputs the stream data (water mole fraction, temperature) to MATLAB, and the time taken to transmit the optimized parameters back is < 30 seconds.

[0094] In this example, the median simulated crystal particle size is 102.1 μm (experimental value 103.6 μm), and the error compared with the experimental result is 1.4%. The simulation results are similar to the experimental results and can guide the experiment.

[0095] Comparative Example 1 and Comparative Example 2

[0096] The other steps are similar to those in Example 3, except that in the simulation process of Comparative Example 1, there is no cooperation with MATLAB, and the solubility model used is the solubility model after all the water has been added. In the simulation process of Comparative Example 2, there is cooperation with MATLAB, but the solubility model used is the dynamic solubility model after the first addition of water.

[0097] Table 1 presents the specific data summary of Examples 3 - 5 of the present invention and Comparative Examples 1 - 2. The experimental results and simulation results in the table were obtained at the same antisolvent addition rate, and the experimental results were detected under continuous addition of the antisolvent.

[0098] Table 1

[0099]

[0100] It can be seen from the data in the table that the errors between the simulation results and experimental results of the examples of the present application can all be controlled within 6%, and the target product, α - form azelaic acid, can be obtained. The preferred addition rate of the antisolvent is 20 mL / min, and the simulation accuracy is relatively high. When the addition rate of the antisolvent is too low, it cannot guide the actual experimental process under its operating conditions.

[0101] The method of the present invention is also applicable to the simulation of other process procedures in the field of antisolvent crystallization, such as the crystallization processes of p - coumaric acid in methanol + water (antisolvent), azlocillin sodium in methanol + acetone (antisolvent), amitriptyline hydrochloride in ethanol + isopropyl ether (antisolvent), chlorpropamide in ethanol + water (antisolvent), glycine in water + ethanol (antisolvent), etc., where the solubility of the solution is changed by adding a poor solvent. In addition, this model is also applicable to the composite processes coupled with antisolvent crystallization, such as the reaction - antisolvent crystallization process, etc.

[0102] The above - mentioned are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

[0103] Matters not described in the present invention are well - known technologies.

Claims

1. A cooling-dissolution coupled crystallization simulation optimization method based on Aspen Plus V14 and MATLAB, characterized in that: The optimization method comprises the following steps: S1. A process flow of a cooling crystallizer and a dissolution crystallizer in series was constructed in Aspen Plus V14, wherein the cooling crystallizer was configured to generate a supersaturated solution by programmed cooling, and the dissolution crystallizer was configured to add a dissolution agent in batches to dynamically control the solvent composition; S2. Building a dynamic solubility model based on MATLAB, including bivariate solubility prediction based on solvent composition and temperature and fitting the Apelblat model parameters using nonlinear least squares method; S3. Use COM technology and ActiveX automation server to establish a real-time data interaction interface between Aspen Plus V14 and MATLAB, so that Aspen Plus V14 can output the solvent composition, temperature and supersaturation data of the dissolution crystallizer to MATLAB. MATLAB will return the fitted Apelblat model parameters to Aspen Plus V14 to update the dynamic solubility model of the dissolution crystallizer. S4. During the process of adding the solvent, step S3 is cyclically executed until the addition of the solvent is completed or a preset termination condition is reached.

2. The optimization method according to claim 1, characterized in that: Adding the solvent in batches includes: Discretize the dissolution agent addition process into 80-150 batches; The amount of solvent added in a single batch is 0.8%-1.25% of the total amount added.

3. The optimization method according to claim 1, characterized in that: The optimization method is used for the cooling-dissolution coupled crystallization simulation of azelaic acid α-crystal, the dissolution agent is water, and the triggering condition for adding the dissolution agent in batches is to simultaneously meet the following two conditions: The temperature of the dissolution crystallizer is ≤10℃; After each batch of dissolving agent is added, the molar fraction of the dissolving agent in the solution is ≤50%.

4. The optimization method according to claim 1, characterized in that: The dynamic solubility model includes: The effect of solvent composition on solubility is calculated using the CNIBS / RK model, using the formula: ln x=k0+k1w+k2w 2 +k3w 3 +k4w 4 Where x is the mole fraction solubility, w is the mole fraction of water in the solvent, and k0-k4 are fitting parameters; The temperature dependence is described by the Apelblat model, as follows: Where T represents temperature (K), and the Apelblat model parameters A, B, and C are fitted using the least squares method using MATLAB.

5. The optimization method according to claim 4, characterized in that: When fitting the Apelblat model parameters, the initial parameter range is A∈[0,2], B∈[-2,0], C∈[0,2], and R 2 >0.95 is the convergence condition.

6. The optimization method according to claim 1, characterized in that: The data transmission delay of the real-time data interaction interface is less than 1 second, and the interaction frequency is completed within 10 seconds after the end of each batch operation; the batch interval time is industrial grade ≤2 minutes, and the volume of the dissolution crystallizer is ≥10L; the addition rate of each batch of dissolution agent is 20-100mL / min.

7. The optimization method according to claim 1, characterized in that: The preset termination condition includes at least one of the following conditions: The total amount of solvent added is 4 times the amount of the initial solution; The molar fraction of water in the solution of the dissolution crystallizer is ≥50%.

8. The optimization method according to claim 1, characterized in that: The optimization method can be used in the simulation of the crystallization process of changing the solubility of the solution by adding a poor solvent, including trihydroxycinnamic acid in methanol + water (solvent), azlocillin sodium in methanol + acetone (solvent), amitriptyline hydrochloride in ethanol + isopropyl ether (solvent), chlorpropamide in ethanol + water (solvent) or glycine in water + ethanol (solvent).

9. A cooling-dissolution coupled crystallization simulation optimization system based on Aspen Plus V14 and MATLAB, used to implement the optimization method according to any one of claims 1 to 8, characterized in that: include: Aspen Plus V14 module, configured for process modeling and batch solvent control; MATLAB module, configured for dynamic solubility model building and parameter optimization; Data interaction engine, based on COM technology and ActiveX server, realizes real-time communication between Aspen Plus V14 and MATLAB.

10. The optimization system according to claim 9, characterized in that: The configuration of the MATLAB module is: When the water content in the solution of the dissolution crystallizer is detected to be ≥50%, the dissolution agent addition rate and temperature parameters are locked; Trigger exception handling mechanism, including parameter rollback and alarm functions.

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