AUTOCLAVING STERILIZATION SYSTEM

Abstract

This invention relates to an autoclaving system (100) with an artificial intelligence for optimizing sterilization processes. The system includes an autoclave chamber (101) equipped with a sensor array (102) to monitor parameters like temperature, pressure, and humidity. A microprocessor (103) with an AI module (104) analyzes sensor data to generate and adjust sterilization cycles dynamically. The system features an adaptive control unit (105) for fine-tuning sterilization parameters, self-learning capability (107) for continuous improvement, and networking (108) to coordinate multiple autoclaves. A user interface (106) provides real-time monitoring, manual control, and augmented reality maintenance support. It also offers predictive maintenance and remote monitoring through a mobile application (113). The AI module leverages neural networks, reinforcement learning, and fuzzy logic to optimize cycles based on load characteristics, ensuring efficiency and regulatory compliance. The system integrates with renewable energy sources and smart grids, promoting sustainable operations.

Specification

Description:AI-controlled autoclaving system is applying for artificial intelligence and adaptive technologies for reaching the highest performance level. Inside this system comprise the autoclave chamber 101, sensor array 102 and microprocessor 103. The AI module 104, placed inside the microprocessor, continuously analyses the real-time sensor data to optimize sterilization cycles by temperature, and dynamically adjusting time 109.

The user interface (106) allows users to have real-time data in the presentation, manual control options, and augmented reality features that support maintenance functions. The self-learning capability (107) is through some algorithms to enhance the refinement of the same based on each cycle from historical data. Predictive maintenance ensures smooth operations by giving an equipment failure forecast and providing preventive recommendations.

The system allows the network capabilities (108), in which many autoclaves are coordinated to work in synchronism and support maintenance work by workload. Connection with mobile applications (113) is made, thus allowing the process to be remote-controlled as well as the action being taken in relation to it. The system is designed to work even with renewable sources of energy and smart grids to optimize energy consumption. This combination of AI-module 104 control, self-learning, and real-time monitoring of the process ensures its high efficacy in sterilization, continuous improvement in the process, and minimization of energy consumption.

The autoclaving system is the advanced, intelligent device that optimizes the sterilization process by using an artificial intelligence (AI), real-time sensor data, adaptive controls, and interconnected functionality. Each component of the autoclaving system 100 is implemented with sterilization efficacy and efficiency to seamlessly set into medical, laboratory, and industrial environments where precise and reliable results are always importrant. Below are descriptions of all the components:

1. Autoclave Chamber (101)
This chamber is made of high-strength, heat-resistant material, which is able to support extreme pressure and temperature, to handle high-temperature steam sterilization. Inside this chamber, items to be sterilized are placed and the entire inside environment is monitored and maintained in such a way that there is equal exposure of steam and heat throughout the area. Chamber design has ports for sensors and a door which has sealing mechanisms that avoids leakage during the sterilization cycle. Autoclave Chamber 101 features enable even steam distribution so all the articles in the load are covered uniformly.

2. Sensor Array (102): A sensor array 102 is a set-up of sensors that are strategically placed to monitor important parameters, such as temperature, pressure, and humidity. It is disposed within the autoclave chamber (101) and collects real-time data at several points inside the chamber, which enable an assessment that is extremely accurate of the conditions within the chamber. Each sensor sends their input to the system's microprocessor (103). An AI module 104 scans the inputs and processes control, if needed. Such sensor array ensures that such environment in the chamber achieve extremely stringent requirements set above to guarantee effective sterilization.

3. Microprocessor (103)
The microprocessor (103) is the processing unit of the autoclaving system 100 for connecting point between the sensor array (102), the AI module (104), and the adaptive control system (105). It receives data from sensors, filters, and removes noise from the data to remove wrong information and then uses such data for making decisions within the system. It is a real-time data interpreter, and it connects directly to the AI module (104) so that it receives the precise measurements needed for the AI to make wise decisions. The microprocessor 103 also highly contributes to executing commands from the AI module, relaying instructions to the adaptive control system (105) to change parameters that include temperature, steam pressure, and the cycle duration according to the actual real-time conditions that exist within the chamber.

4. AI Module (104). The AI module (104) is the advanced component of artificial intelligence inside the microprocessor (103). It includes algorithms which process sensor data in real time combined with sterilization historical data and allows the AI module to do intelligent adjustment in the sterilization parameters based on such information. The AI module suggest sterilization settings for different loads based on the pattern analysis of previous cycles so that each cycle's duration, temperature, and pressure are optimized by the size, composition, and level of contamination expected in the load. The AI module 104 sense anomalies or irregularities in data and respond appropriately by adjusting parameters to bring conditions back to optimal levels, thereby ensuring a consistent, high-quality sterilization result.

5. Adaptive Control System (105)
The adaptive control system 105 work on the instructions coming from the AI module 104, and makes dynamic variation of parameters such as the cycle time, temperature and steam pressure keeping the optimal conditions within the chamber. It continuously communicates with the microprocessor 103 in charge of the physical regulation of the autoclaving process. For example, if the AI module finds that it is slightly lower than what it has been set at the thermostat, the adaptive control system boost steam pressure to raise the temperature more rapidly. On the other hand, when conditions are too extreme, it made to lower pressure or reduce cycle time in a bid not to damage fragile items. This real-time adaptability ensures that each cycle caters to the specific sterilization requirements of the load.

6. User Interface (106)
The user interface 106 is a front window and control panel on the autoclave, whereby the operator gets real-time feedback on the sterilization process. Here, information such as current temperature, pressure, cycle time, and any alerts generated by the system is seen. Operators monitor cycle progress and, if need be, adjust settings manually or interrupt the cycle using this interface. It also allows operators to initiate new cycles or select predefined sterilization parameters based on the load. The user interface 106 makes it more user-friendly and affords the user much more control over the system, thus ensuring that there is transparency during the sterilization process.

7. Self-Learning capability (107)
The self-learning capability 107 of the AI module (104) relates to a system's adaptability that change over time and increasing its level of performance. The AI uses a feedback loop to log the outcomes of every sterilization cycle. The system learns from each result and uses it in order to change future cycles. For example, if a specific kind of load requires a longer time before the sterilization is accomplished. Automatically, the system changes future cycles for that kind of load. This self-learning capability 107 ensures that the system is more efficient and accurate with time, thus reducing the cycle duration and energy usage and maintaining high standards for sterilization. This feature makes the autoclave particularly valuable in environments where varying requirements for sterilization are prevalent.

8. Networking Capability (108)
The networking capability (108) allows the autoclaving system 100 to connect with other autoclaves or a centralized control system, and also allowing coordinated operation across many units. This networking feature allows for sharing of the autoclave cycles, efficient loading, and discussion of cycle parameters and outcome. For large facilities, this is very important for remote monitoring and managing multiple autoclaves from one location. The networking capability 108 also provides under any type of maintenance issues or errors, with remote diagnostics and alerting that minimize downtime and allow smooth operations.

Interconnected Functionality
The various components of the autoclaving system function with effectiveness to ensure a stress-free sterilization process, the sensor array (102) monitors the conditions continuously within the autoclave chamber (101). It feeds this information to the microprocessor (103). The processes the data and sends it over to the AI module (104), that uses this input to make more adjustments. With information from the decisions of the AI, the adaptive control system 105 adjusts in real time to maintain the optimal conditions of sterilization.

The user interface (106) makes all the cycle information and controls accessible for the process operators to observe or modify the process if needed. Through this self-learning capability (107), the AI module 104 learns from each cycle, which results in a higher accuracy and decreases time taken in sterilization. The networking capability (108) hooks the autoclave with other units so that coordination is enhanced at facility level along with remote management.

Together, these parts make up a smart, responsive, and efficient autoclave system, yielding consistent high-quality results in the autoclaving process because of its adaptability in dealing with a wide array of loads and conditions. This design is significant in sterilization technology, as AI and adaptive controls optimize both the effectiveness and efficiency of its performance.

Embodiments:
1. Sensor Array (102)
The autoclave chamber 101 is furnished with a highly sensitive sensor array that continuously measures many parameters:
- Temperature sensors: High-precision thermocouples placed at several points in the chamber to ensure proper, uniform distribution of heat.
- Pressure sensors: Measures the steam pressure at all stages.
- Humidity sensors: Measuring the moisture levels to ensure that the steam is fully saturated.
- Load sensors: Measures the weight and how it is distributed within the chamber.
- Conductivity sensors: Measures the conductivity or characteristics of the load material.
These sensors give real-time information to the microprocessor, allowing precise monitoring and control of the sterilization process.
2. Microprocessor (103)
The high-performance microprocessor 103 is basically acting like a central processing unit to accept data fed into it from sensor array 102 and then carry out processing plus AI module 104 execution. It controls all the actuators and valves present inside the autoclave by virtue of manifold control interactions-all driven by the microprocessor 103. It contains redundancy plus fail-safe mechanisms for guaranteed satisfactory performance during system operation.

3. AI Module (104)
The AI module 104 is a complex, piece of software running on the microprocessor 103. It applies a variety of machine learning techniques, including:
- Neural networks: To recognize patterns and predict the best sterilization parameters.
- Reinforcement learning: To incrementally update decisions based on the outcome.
- Fuzzy logic: To make decisions where the input data are ambiguous or imprecise, and to provide subtle incremental changes.
The AI module 104 takes sensor data and compares it with its database of ideal sterilization profiles to make real-time decisions to adjust autoclaving parameters.

4. Adaptive Control System (105):
Adaptive control system 105 dynamically adjusts, on the basis of AI module's 104 decisions:
• Steam injection rate and duration
• Temperature levels and ramp rates
• Cycle duration and phases
• Pressure levels
This ensures that each load receives a customized sterilization cycle optimized for its specific characteristics.

5. User Interface (106):
A touch-screen display provides operators with:
- Real-time visualization of the sterilization process
- Alerts and notifications
- Manual override options
- Historical data and trend analysis
The interface is designed to be intuitive and user-friendly, allowing for easy operation and monitoring.

6. Self-Learning Capability (107)
After each sterilization cycle, the system:
- Analyzes the cycle data
- tolerate comparisons with the expected result
- It enriches its knowledge base
- Refines its algorithm of decision-making
- This continuous learning process led to improvements continuously in efficiency and effectiveness.

7. Networking Capability (108)
The system connects to other autoclaves in a facility through a secure network. This enables:
-Load balancing through multiple units.
-Coordinated scheduling for maintenance.
-Facility-wide data analysis and reporting.

8. Predictive Maintenance
The system is able to predict outcomes by having a trend analysis of the operational data over time.
- Predict failures of autoclave parts
- Design preventive servicing
- Design optimum spares stock

9. Autoclave chamber (101)
This model is a standalone autoclave system equipped with the AI control system. This works with a small-scale facility or be as the pilot implementation.

10. Networked autoclaves (112)
Several networked, AI-controlled autoclaves can be included in a network to organize activities and optimize resource usage in a bigger facility.

11. Mobile Application (113)
A secure mobile application facilitates remote monitoring and basic control of the autoclaving system to allow operators to receive alerts and check status anywhere.

12. Cloud-Based Data Analysis
This representation includes a safe cloud connection for transferring anonymous cycle data, which means big data analysis can be done and performance tuned over the entire network at the facility.

Additional Embodiments
Augmented Reality Support for Maintenance:
Integration of AR devices provides maintenance engineers with visual support and real- time information at the servicing and repairing jobs.

Voice Control and recognition:
Voice recognition technology is enabled use without having to physically touch the equipment and status queries-an important feature in closed sterile environments.

Modular Sensor System:
A plug-and-play sensor array is upgraded and customized for specific applications or advancements in sensing technology.

Green Energy Interface:
This form comprises interfaces to renewable energy sources and smart grid systems, thusoptimizing energy usage as per the availability and cost.
-Operation of the AI-Controlled Autoclaving System 100

Load Insertion and Initial Analysis:
The operator places items to be sterilized into the autoclave chamber.
- Once sealed, the sensor array (102) immediately begins monitoring the characteristics of the load.

AI Based Cycle Planning:
The AI module (104) commences processing the data from the sensors and determines the optimum parameters for the sterilization process
It creates a cycle plan as per the density of the load, its composition of material, and the sterility assurance level
Adaptive Cycle Execution:
- The adaptive control system (105) initiates the sterilization cycle based on the AI- generated plan.
- In the run of cycle, continuous data feed is sent to the microprocessor by sensor array 106.
- The AI module 104 continuously monitors real-time data and adjusts time (109), temperature (110) and steam parameters (111) as per the need.

Monitoring and Intervening by User:
- User interface (106). Real time cycle information is in the operator monitor.
-Intervention or parameter adjustment by operators, if needed can be made through the interface

Cycle Completion and Verification:
The system analyses last for confirmation of proper sterilization after cycle completion.
- If the cycle is successful, the system provides this information. If the cycle fails, the system communicates its failure to the operator and recommends reprocessing.

Data Logging and Learning:
-All information from cycles is logged for record keeping and mandated regulatory needs.
-Self-learning capability (108) processes the outcomes from a cycle and refines its knowledge base to make more appropriate choices in subsequent cycles.

Networked Operation (if applicable):
- In a multi-autoclave configuration, the system interacts with other equipment to control the equipment at the facility level, such as scheduling and energy use of loads.
Maintenance Upgrade:
- A self-diagnostic system continuously monitors its performance, monitors its own predictive maintenance, schedules the service, and records the requirement.
- Software upgrades may be installed safely to enhance functionality and introduce more sterilization cycles.

Methods of performing the invention
1. System Setup and Calibration:
-Fit the advanced sensor array (102) inside the autoclave chamber 101 in such a way that full coverage is achieved.
-Calibrate all sensors to assure high accuracy levels in their operational ranges.
- Instruct the installation of microprocessor (103) and AI module (104) software, with proper integration with the autoclave's mechanical systems.
-Configure the user interface (106) and test that all display and control functions are operational.

2. AI Training
-A complete set of sterilization profiles covering a wide range of load types and materials are populated into the database inside the AI module.
- Run several test cycles of load configurations to calibrate the AI algorithms for judgment.
3. Load Processing:
-Load items into the sterilizing autoclave ensuring uniform packing for deep penetration of steam.
-Close the autoclave door, which will now activate the automatic load analysis of the sensor array (102).
- Let the AI module 104 prepare an optimal cycle plan according to load character.
-Monitor and control the process dynamically when a sterilization cycle is initiated by adaptive control system 105.

4. Real-time Monitoring and Adjustment
The user interface 106 enables the monitoring of sterilization progress by displaying real-time values for temperature, pressure, and stage progression.
- Allow the AI module to dynamically change the time (109), temperature (110), and steam (111).
-Intervene manually only when it is unavoidable by utilizing the override functions in the interface.

5. End Cycle and Validation
-After the cycle completion, validate the data from sterilization effectiveness provided by the system.
- Biological indicator tests are periodically be conducted to verify the functionality of the system.

6. Data Analysis and System Learning:
-Each cycle has the self-learning capability (107) analyze the cycle data and update the knowledge base.
-Periodic review of system performance metrics for continued improvement.
7. Networked Operation:
- In multi-autoclave setups, use the networking feature (110) to coordinate workflows on all units.
-Use load balancing techniques to achieve maximum throughput and power economies.

8. Servicing and Updates:
-Follow the recommended prediction servicing schedule set by the system.
-Update the software in the AI module 104 periodically to update the latest protocols of sterilization and improvements on the algorithm.

9. Compliance and Reporting:
- The use of the system report function is providing cycle reports for each sterilization cycle carried out.
-Reports are retained, by the system user, for both regulation compliance and quality control.

10. Continuing Improvement:
Trend Data from extended performance is identify and highlight areas for improvement.
Collaborate with the system manufacturer to comment on the system and help in preparation for future revisions.
, Claims:1. An autoclaving system 100 for optimizing sterilization processes, comprising:
a) an autoclave chamber 101;
b) a sensor array (102) within said chamber for monitoring sterilization parameters;
c) a microprocessor (103) connected to said sensor array 102;
d) an AI module (104) implemented on said microprocessor 103 for analyzing sensor data and controlling sterilization parameters;
e) an adaptive control system (105) for dynamically adjusting time, temperature, and steam parameters based on AI module decisions;
f) a user interface (106) for displaying process information and allowing manual interventions;
g) a self-learning capability (107) for continuously improving system performance;
h) a networking capability (108) for coordinating multiple autoclaves;
wherein said AI module (104) dynamically optimizes sterilization cycles based on real-time load characteristics and historical data.

2. A method for autoclaving system as claimed in claim 1, comprising the steps of:
a) analyzing load characteristics using a sensor array (102);
b) generating an optimal sterilization cycle plan using an AI module (104);
c) executing said cycle plan while continuously monitoring sterilization parameters;
d) dynamically adjusting time (109), temperature (110), and steam (111) parameters based on real-time data analysis;
e) verifying sterilization efficacy upon cycle completion;
f) logging cycle data and updating the AI module's knowledge base;
g) coordinating operations with other networked autoclaves (112) if applicable.

3. The system as claimed in claim 1, wherein said sensor array (102) comprises temperature sensors, pressure sensors, humidity sensors, load sensors, and conductivity sensors.

4. The system as claimed in claim 1, wherein said AI module (104) employs neural networks, reinforcement learning, and fuzzy logic algorithms.

5. The system as claimed in claim 1, wherein said self-learning capability (107) analyzes cycle outcomes and refines decision-making algorithms after each sterilization cycle.

6. The system as claimed in claim 1, further comprising a predictive maintenance feature that analyzes operational data to forecast component failures and schedule preventive maintenance.

7. The system as claimed in claim 1, wherein said networking capability (108) enables load balancing, coordinated maintenance scheduling, and facility-wide data analysis across multiple autoclave units.

8. The method as claimed in claim 2, further comprising the step of generating detailed cycle reports for quality assurance and regulatory compliance purposes.

9. The system as claimed in claim 1, further comprising a mobile application 113 for remote monitoring and basic control of the AI -controlled autoclaving system 100.

10. The system as claimed in claim 1, wherein said user interface (106) includes augmented reality features for maintenance support.

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