AN ARTIFICIAL INTELLIGENCE DRIVEN PHOTO BIOREACTOR SYSTEM FOR CULTIVATING ALGAE

Abstract

7. ABSTRACT The present invention provides an AI-driven photo bioreactor system (100) optimized for scalable algae cultivation. It includes a helix-shaped reactor (102) that maximizes light penetration and CO₂ absorption, enhancing photosynthesis. An AI module (104) continuously monitors and adjusts environmental parameters, such as light intensity, temperature, and nutrient levels, for optimal growth of specific algae strains. The system also integrates a CO₂ capture module (106), enabling direct use of industrial CO₂ emissions, creating a carbon-negative process. Its modular structure (108) allows flexible scalability for various operational needs, from small farms to large industrial facilities. Additionally, an LED lighting system (110) and a closed-loop system (112) for water recycling reduce resource consumption. This invention offers a sustainable, cost-effective platform for producing algae-based biofuels, bio plastics, and animal feed, contributing to global carbon reduction goals. The figure associated with abstract is Fig. 1.

Specification

Description:4. DESCRIPTION
Technical Field of the Invention

The present invention related to bioreactor technology. More particularly, focusing on the development of AI-driven photo bioreactors for algae cultivation, incorporating CO2 capture technology.

Background of the Invention

The accelerating concerns about climate change and the rapid depletion of fossil fuels have heightened the demand for sustainable and renewable resources across industries. Algae, a highly efficient biological system capable of converting carbon dioxide (CO₂) into biomass through photosynthesis, offers a promising bioresource. This biomass has extensive applications, including biofuels, bioplastics, pharmaceuticals, and animal feed. However, existing methods for algae cultivation face a host of critical challenges that significantly restrict their scalability, efficiency, and economic feasibility. Consequently, although algae hold immense potential as a renewable resource, practical and scalable solutions for its industrial-scale cultivation are still lacking. This gap presents a compelling need for advanced, economically viable, and environmentally friendly systems that can transform algae into a feasible source of biomass for sustainable applications.

Traditionally, algae cultivation has been conducted in open pond systems due to their simplicity and low initial costs. While this method has been effective in small-scale applications, open pond systems suffer from several inherent limitations that undermine their viability at an industrial scale. The most prominent issue is their dependency on large land areas, which is a scarce resource in many regions. Furthermore, open systems are prone to contamination from various environmental factors, including fluctuating weather conditions, pollutants, and the invasion of unwanted microorganisms. This exposure can lead to inconsistent yields and reduced quality of algae biomass, rendering the process highly inefficient. Another significant drawback of open pond systems is the limited control over critical growth parameters, such as light intensity, temperature, and nutrient concentration. Without precise control, it becomes difficult to achieve optimal conditions for algae growth, particularly when targeting specific algae strains that thrive under unique environmental parameters.

To overcome some of the limitations of open pond systems, closed photobioreactors have been developed. These reactors offer better environmental control, which allows for more consistent and predictable algae growth. However, the costs associated with closed photo bioreactors are substantially higher, both in terms of initial investment and maintenance. The complexity of these systems makes them less accessible for smaller producers or industries with limited budgets. Additionally, many existing closed photo bioreactors are constrained by designs that inadequately optimize light penetration and CO₂ absorption, which are essential for maximizing biomass yield. At an industrial scale, these inefficiencies translate to higher operational costs and reduced output, making current photo bioreactor designs less practical for widespread adoption.

Another major shortfall in existing algae cultivation systems is the limited integration of advanced technologies that could otherwise enhance productivity and resource utilization. Many current systems still depend heavily on manual monitoring and control, which leads to suboptimal growth conditions and fails to harness the potential of technological innovations in real-time optimization. Without automated systems to continuously adjust environmental variables, algae strains are unable to achieve their full growth potential, particularly when specific compositions, such as lipid-rich strains for biofuel production or protein-dense strains for animal feed, are desired. The adaptability of these systems is further limited by their inability to optimize cultivation conditions for diverse industrial applications, thereby reducing their overall versatility and appeal to a broader market.

Several prior inventions have attempted to address these issues, but they too have limitations that prevent them from fully realizing the potential of algae as a bio resource. For instance, US Patent No. 11801475B2 describes a microalgae carbon fixation system with adjustable light panels, but it remains highly dependent on natural environmental conditions. Such dependency reduces the system's adaptability across varied climates and restricts its potential for consistent productivity. Another prior art, US Patent No. 20230365905A1, presents a combined bioreactor and CO₂ capture system using a parabolic trough collector. While this design offers some improvement in algae production, its complex construction poses challenges for scalability and widespread industrial application. Similarly, US Patent No. 20130288228A1 introduces a biomass cultivation method incorporating a heat sink for growth parameter optimization, but the reliance on external temperature control arrays limits scalability and increases energy costs.

Additional prior art, such as WO2024144390A1, explores the use of waste products in algae cultivation via carbonation reactors. Although this approach integrates waste management, the complexity and resource requirements of the setup create efficiency and cost-effectiveness challenges in broader applications. Another patent, CN110684644A, describes a photo bioreactor with waterproof LED lighting at various wavelengths to optimize light efficiency. However, this design lacks comprehensive environmental control, limiting its effectiveness in cultivating algae strains across diverse conditions and hindering scalability.

Given the current limitations of both open and closed photo bioreactor systems, there is a significant need for a solution that combines the advantages of both approaches while minimizing their respective disadvantages. A viable system would need to offer precise, real-time control over environmental conditions to support optimal algae growth. It would need to incorporate advanced technologies, such as artificial intelligence, to automate and optimize the cultivation process dynamically. Additionally, a sustainable approach to CO₂ integration would be essential, providing an opportunity to use industrial CO₂ emissions as a feedstock for algae, thereby creating a carbon-negative process. Scalability is also crucial, as a modular design could make it accessible for both small-scale and industrial operations, thus bridging the gap between efficiency and cost-effectiveness in algae cultivation.

In response to these critical challenges, the present invention introduces an AI-driven photo 122520bioreactor system designed to optimize algae growth through real-time control of key environmental parameters, including light, temperature, and nutrient levels. By integrating CO₂ capture technology and a modular design, this system promises to be a scalable, efficient, and sustainable solution, addressing the urgent need for a practical algae cultivation platform that supports diverse industrial applications.
Brief Summary of the Invention

The following presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

One primary objective of this invention is to create a scalable and cost-effective photo bioreactor system specifically designed to optimize algae growth through AI-driven real-time control of environmental parameters such as light intensity, temperature, nutrient levels, and CO₂ concentration. Algae's unique ability to convert CO₂ into valuable biomass makes it an ideal candidate for sustainable bio-based industries. By integrating artificial intelligence (AI) into this photo bioreactor system, the invention aims to dynamically adapt to varying growth needs and maximize biomass yield in a resource-efficient manner.

Another objective of the invention is to utilize industrial CO₂ emissions as a feedstock, creating a carbon-negative process by integrating CO₂ capture technology directly into the photo bioreactor. In capturing CO₂ emissions from external industrial sources, this system provides a sustainable solution that not only mitigates greenhouse gas emissions but also supports the production of valuable algae biomass for biofuels, bio plastics, animal feed, and other applications. This innovative approach not only addresses global carbon emission issues but also positions algae as a valuable resource in the transition toward a carbon-neutral future.

The invention also aims to provide a photo bioreactor with a modular and scalable design, making it adaptable for various operational scales. This modular configuration allows the system to grow in capacity easily by connecting multiple reactors, facilitating seamless scalability from small-scale algae farms to large industrial facilities. This adaptability makes it suitable for both independent producers and large industries, providing flexibility for different production demands. Additionally, the modular design facilitates easier maintenance, expansion, and customization, ensuring that the system can support diverse market needs and increase algae cultivation capacity.

Another critical objective of the invention is to maximize algae productivity by optimizing light exposure, CO₂ absorption, and nutrient distribution within the photo bioreactor. Traditional flat-panel and open pond designs often face limitations in light penetration and CO₂ diffusion, leading to lower efficiency and restricted growth. This system's unique helix-shaped reactor structure overcomes these issues, allowing for more effective distribution of light and CO₂ across the algae culture. By enhancing these conditions, the invention promotes higher photosynthetic efficiency and faster growth rates, significantly improving biomass yield over existing technologies.

A further objective of the invention is to lower the economic barriers to algae production by using efficient hardware coupled with AI technology for automated monitoring and adjustment of cultivation parameters. The invention integrates a programmable LED lighting system that optimizes energy consumption and minimizes shading, reducing operational costs and environmental impact. Furthermore, the closed-loop water and nutrient recycling mechanism minimizes resource waste, making the system economically viable for widespread adoption in industries seeking sustainable, high-yield algae cultivation.

According to an aspect of the present invention, an AI-driven photo bioreactor system for cultivating algae is disclosed. This innovative system combines a helix-shaped reactor with advanced AI modules, a CO₂ capture mechanism, and a modular structure to enable efficient and sustainable algae production. By utilizing machine learning algorithms to monitor and adjust key environmental factors, the system maintains ideal conditions for algae growth, thus optimizing biomass yield for a range of applications, including biofuels, bio plastics, and animal feed. The inventive design and technology address the limitations of traditional algae cultivation methods by delivering a practical and scalable solution that is both sustainable and economically viable.

The AI-driven photo bioreactor system comprises a helix-shaped reactor designed to maximize light penetration and CO₂ absorption for optimal algae growth. The helical structure increases surface area, allowing light to penetrate efficiently throughout the algae culture. This structure contrasts with flat-panel or open pond designs, where uneven light exposure and CO₂ diffusion can hinder growth. The unique helical configuration ensures that algae cells across the culture receive equal exposure to light, minimizing shading effects and promoting high photosynthetic efficiency. As a result, this design significantly enhances algae productivity within a smaller spatial footprint.

The AI module, which continuously monitors environmental parameters such as light intensity, nutrient levels, temperature, CO₂ concentration, pH, and dissolved oxygen. Using machine learning algorithms, the AI module dynamically adjusts these parameters to maintain ideal conditions based on real-time data, tailoring the environment to support the specific needs of different algae strains. By identifying optimal conditions for various applications, such as high-lipid strains for biofuels or protein-rich strains for animal feed, the AI module ensures that each cultivation cycle yields the highest possible biomass for the intended use. This level of control allows the system to fine-tune growth conditions, thereby maximizing efficiency and consistency across production cycles.

The system also includes an innovative CO₂ capture module that directly channels industrial CO₂ emissions into the photo bioreactor, converting waste gases into algae biomass. This integration with industrial CO₂ sources provides a sustainable way to reduce greenhouse gas emissions while supporting algae cultivation. By sequestering CO₂ and turning it into a valuable resource, the invention contributes to carbon-negative operations, aligning with global sustainability goals. The CO₂ capture module works alongside the AI module to regulate CO₂ levels within the reactor, ensuring that the algae have sufficient carbon for growth without oversaturating the environment.

In another aspect, the invention incorporates a modular structure that enables scalable production capacity. This modular design allows multiple reactors to be connected in parallel or in a stacked configuration, expanding production capacity as needed. With this flexibility, the system can be tailored to meet various operational requirements, from small farms to large industrial facilities. Each reactor module can be independently monitored and controlled, providing operators with the ability to optimize production based on specific needs. This modularity not only simplifies scaling but also enhances operational adaptability, making the system suitable for diverse market demands.

The LED lighting system integrated within the photo bioreactor, which provides consistent and programmable light exposure. The LED lighting array is designed to simulate natural light cycles or specific wavelength patterns tailored to algae growth. The AI module adjusts light intensity and spectrum based on real-time growth data, ensuring that algae cells receive the precise light conditions required for optimal photosynthesis. This efficient lighting strategy minimizes energy consumption, thereby reducing operational costs and environmental impact while promoting faster growth and higher yields.

The system's closed-loop design further enhances sustainability by recycling water and nutrients, reducing resource waste. The closed-loop mechanism captures evaporated water, condenses it, and reintroduces it into the reactor, which minimizes water consumption-a critical feature for sustainable large-scale operations. Unused nutrients are also recirculated, ensuring they remain available for the algae culture. This closed-loop approach not only conserves resources but also minimizes environmental impact, making the system economically and ecologically sustainable.

The invention includes a cloud-based data storage system that records all operational data, including environmental conditions, algae growth metrics, and resource consumption. This data is accessible via a user interface, allowing operators to monitor and adjust the system remotely. The data storage system supports detailed tracking and analysis, enabling long-term optimization of the algae cultivation process. By analysing historical data, the AI module continuously improves its algorithms, making the system smarter and more efficient over time.

The AI-driven photo bioreactor system thus offers a comprehensive solution for efficient algae production. Its scalable, modular design, combined with advanced AI-driven optimization, provides a flexible, high-yield platform for sustainable algae cultivation. By leveraging industrial CO₂ emissions as a resource, the system not only enhances algae productivity but also aligns with global efforts to reduce carbon footprints. This innovative system represents a major advancement in algae cultivation technology.

Further scope of applicability of the present invention will become apparent from the detailed description given hereinafter. However, the detailed description and specific examples, while indicating preferred embodiments of the invention, will be given by way of illustration along with complete specification.

Brief Summary of the Drawings

The invention will be further understood from the following detailed description of a preferred embodiment taken in conjunction with an appended drawing, in which:

Fig. 1 illustrates an AI-driven photobioreactor system (100) for cultivating algae, in accordance with an exemplary embodiment of the present invention;

Fig. 2 illustrates a detailed view of the helix-shaped reactor, emphasizing the spiral structure designed to maximize light penetration and CO₂ absorption for enhanced algae growth, in accordance with an exemplary embodiment of the present invention;

Fig. 3 illustrates the AI module, which continuously monitors and adjusts growth conditions by processing data from embedded environmental sensors, in accordance with an exemplary embodiment of the present invention;

Fig. 4 depicts the CO₂ capture module, which captures and channels CO₂ emissions into the reactor to sustain algae growth and support carbon-negative operations, in accordance with an exemplary embodiment of the present invention;

Fig. 5 presents the GPS-based asset tracking module and yield tracking module, providing real-time data on component locations within the system, in accordance with an exemplary embodiment of the present invention;

Fig. 6 demonstrates the integration of external data sources, such as climate sensors and irrigation systems, feeding data into the system for dynamic environmental adjustments, in accordance with an exemplary embodiment of the present invention;

Fig. 7 illustrates the user interface, which displays asset locations, productivity heat maps, and irrigation schedules, enabling remote monitoring and control through an interactive dashboard., in accordance with an exemplary embodiment of the present invention;

Fig. 8 illustrates the LED light array system, equipped with wavelength modulation, light intensity control, and programmable lighting, supporting optimal light conditions for photosynthesis across growth stages, in accordance with an exemplary embodiment of the present invention;

Fig. 9 illustrates a schematic of the closed-loop system, showing the water recycling mechanism, including water capture, condensation, recirculation, and nutrient recycling, which minimizes resource consumption, in accordance with an exemplary embodiment of the present invention.

Detailed Description of the Invention

The present disclosure emphasises that its application is not restricted to specific details of construction and component arrangement, as illustrated in the drawings. It is adaptable to various embodiments and implementations. The phraseology and terminology used should be regarded for descriptive purposes, not as limitations.

The terms "including," "comprising," or "having" and variations thereof are meant to encompass listed items and their equivalents, as well as additional items. The terms "a" and "an" do not denote quantity limitations but signify the presence of at least one of the referenced items. Terms like "first," "second," and "third" are used to distinguish elements without implying order, quantity, or importance.

According to an exemplary embodiment of the present invention, an AI-driven photo bioreactor system designed for optimized, scalable algae cultivation by integrating advanced photo bioreactor technology with artificial intelligence (AI) and CO₂ capture capabilities. At its core, the system uses a helix-shaped reactor structure to enhance light exposure and CO₂ absorption, a machine-learning-enabled AI module to monitor and control environmental parameters, and a modular design to support scalability for various production scales. The system further incorporates an LED lighting system and a closed-loop design that minimizes water and nutrient loss. Together, these components create an efficient, sustainable platform for algae cultivation, suitable for applications ranging from biofuels to bio plastics.

The system comprises a helix-shaped photo bioreactor designed to maximize algae productivity by enhancing light penetration and CO₂ absorption. This spiral structure ensures uniform light exposure across the entire algae culture, reducing shading effects that commonly hinder growth in traditional designs. Furthermore, the system integrates a CO₂ capture module that directly channels industrial CO₂ emissions into the reactor, converting waste gases into valuable biomass. By implementing this carbon-negative approach, the system not only optimizes algae growth but also reduces greenhouse gas emissions, supporting environmentally sustainable practices.

The AI module is an integral part of the invention, continuously monitoring environmental parameters like light intensity, temperature, pH, dissolved oxygen, nutrient levels, and CO₂ concentration. By analyzing real-time data through machine learning algorithms, the AI adjusts these conditions to maintain an optimal growth environment. For example, the AI module can identify ideal growth conditions for specific algae strains, such as high-lipid strains for biofuel production or protein-rich strains for animal feed. This adaptability ensures maximum yield and efficiency across various algae applications.

In another embodiment, the invention incorporates a modular structure of the invention enables seamless scalability. Each photo bioreactor module can function independently or in combination with others, creating a flexible and customizable setup suitable for both small and large-scale operations. The modular design also supports easy maintenance and expansion, allowing the system to grow in production capacity as needed without altering the core structure.

Now referring to figures, Fig. 1 (100) illustrates the system, which includes several key components. The helix-shaped reactor (102) is designed to maximize light exposure and CO2 absorption, enhancing algae growth efficiency. The AI module (104) continuously monitors and optimizes environmental factors like light intensity, CO2 levels, temperature, and nutrients to ensure optimal algae growth. The CO2 capture module (106) integrates industrial CO2 emissions into the reactor, converting CO2 into valuable biomass. The system's modular structure (108) allows for scalability, enabling easy expansion of production capacity. The LED lighting system (110) provides consistent and controlled light exposure, while the closed-loop system (112) reduces water loss and conserves resources. All growth data and environmental conditions are tracked by the data storage system (114) for real-time monitoring and optimization.

Fig. 2 illustrates the helix-shaped reactor (102) designed to optimize algae growth through increased surface area exposure to light and CO2. The reactor consists of spirally arranged tubes that allow for efficient light penetration and CO2 diffusion, thereby enhancing the photosynthetic efficiency of the algae. The spiral design also promotes better mixing of nutrients and algae cultures, ensuring uniform growth conditions throughout the reactor.

Fig. 3 illustrates photobioreactor system with the AI-driven module (104), showcasing how various sensors and modules are integrated to optimize algae growth. The central bioreactor contains the algae culture, surrounded by sensors that monitor critical parameters such as light intensity, nutrient levels, temperature, CO2 concentration, and dissolved oxygen. The AI module processes real-time data from these sensors and uses machine learning algorithms to dynamically adjust growth conditions. For example, the AI optimizes light spectra, nutrient dosing, and temperature to enhance biomass yield. Additional feedback loops ensure precise control over environmental variables, promoting efficient and sustainable algae cultivation.

Fig. 4 illustrates the CO2 capture module integrated within the AI-driven photobioreactor system. The CO2 Capture Module (106) is designed to efficiently sequester carbon dioxide from external co-reactor sources, which are connected via pipelines. These external co-bioreactors contribute to capturing CO2 emissions, directing them to the algae photobioreactor for utilization in algae growth. The AI module continuously monitors and regulates the CO2 levels to ensure optimal conditions for photosynthesis and biomass production. The algae within the photobioreactor absorb the CO2, converting it into oxygen and organic matter. This process not only enhances algae productivity but also contributes to environmental sustainability by reducing carbon emissions. The system emphasizes circular resource utilization, combining AI-driven optimization with carbon capture technology.

Fig. 5 presents a schematic view of the AI-driven photobioreactor system, highlighting the GPS-based asset tracking module (116). The diagram illustrates how this module interacts with yield tracking metrics (118) to provide real-time location data of various components within the system. By integrating GPS technology, operators can monitor the positions of modular reactor units (120), facilitating efficient management and optimization of algae cultivation operations. The interaction with yield tracking data enables assessment of individual module performance, allowing for informed decisions to enhance productivity.

Fig. 6 illustrates the integration of external data sources (122) with the AI-driven photobioreactor system. It depicts connections to various components, such as climate sensors (124) and irrigation systems (126), which continuously feed data into the system. This integration allows the system to dynamically adjust environmental conditions-like temperature, light intensity, and nutrient levels based on real-time data from these sources. This responsive capability ensures that the algae cultivation environment is optimized (128), enhancing overall biomass productivity.

Fig. 7 shows the user interface (130) of the AI-driven photo bioreactor system, providing a comprehensive view of real-time data from the farm comprises asset locations (132), productivity heat maps (134), and irrigation schedules (136). Users can easily monitor the performance of various reactor modules (138) at a glance, analyse productivity trends, and manage irrigation efficiently. This interactive dashboard empowers operators to make timely adjustments based on current operational data, ensuring optimal growth conditions for the algae.

Fig. 8 illustrates the LED light array system integrated within the AI-driven photo bioreactor. This system is equipped with multiple LED light panels (140) arranged around the algae culture chamber, providing adjustable light spectra (142) and intensities optimized for algae growth (152). The AI system continuously analyzes real-time data (144) from growth sensors, such as chlorophyll levels, photosynthesis rates, and environmental conditions, to dynamically adjust the wavelength (146) (e.g., red, blue, and green light (148)) and intensity (150) of the LEDs. By fine-tuning light exposure, the system enhances algae productivity (154) and energy efficiency (1565), adapting to various growth phases and environmental changes to promote optimal biomass yield. The modular design allows for targeted illumination to specific areas of the culture chamber, further improving the cultivation process.

Fig. 9 illustrates the flow diagram of a closed-loop system (112) integrated within the AI-driven photobioreactor. The closed-loop system captures evaporated water (158), condenses it (160), and reintroduces it into the reactor (162), ensuring continuous water circulation. The nutrient recycling (164) mechanism collects unused nutrients (166) from the algae culture chamber and reintroduces them into the reactor (168), reducing waste and promoting efficient resource utilization. This sustainable operation reduces both water and nutrient consumption resulting optimization of algae growth.

The manufacturing process for the AI-driven photo bioreactor involves several steps to ensure the system is efficient and functional. First, the helical reactor tubes are molded from a durable, transparent material that allows for maximum light penetration. The tubes are assembled in a spiral configuration to maximize surface area and are fitted with ports for CO₂ injection, nutrient dosing, and sensor placement. The modular reactor frame is then fabricated to enable easy assembly of additional reactor units in parallel or vertical arrangements.

LED lighting arrays are positioned around the helical structure, allowing adjustable intensity and wavelength to simulate natural light cycles or specific growth-promoting wavelengths. The CO₂ capture module is manufactured with filtration systems to purify industrial CO₂ before directing it into the reactor, ensuring algae receive a clean source of carbon. Finally, the AI module and sensors are integrated into the reactor, with all data managed via a cloud-based storage system for remote access and monitoring.

To operate the system, operators first select the appropriate algae strain based on the intended application, such as high-lipid strains for biofuels or high-protein strains for animal feed. The system is then initialized by setting growth parameters in the AI module, which are adjusted automatically based on real-time sensor data. The CO₂ capture module channels industrial CO₂ emissions into the reactor, providing a consistent carbon source. As the algae grow, the AI module continuously optimizes conditions, adjusting light intensity, nutrient levels, and temperature to promote maximum productivity.

Operators can monitor and control the system remotely through a user interface connected to the cloud-based data storage system, which logs all environmental conditions and growth metrics. When the algae reach the desired maturity, they are harvested and processed for the intended application, such as extraction for biofuel or use in bio plastic manufacturing.

The AI-driven photo bioreactor system provides numerous advantages over traditional algae cultivation methods:
• High Efficiency: The helix-shaped design optimizes light exposure and CO₂ absorption, resulting in faster growth rates and higher biomass yields.
• Cost-Effective Production: AI-driven optimization minimizes resource waste, and the use of industrial CO₂ as a feedstock reduces operational costs.
• Sustainability: The system is carbon-negative, as it captures CO₂ emissions and converts them into biomass, contributing to reduced greenhouse gas levels.
• Scalability: The modular structure allows the system to adapt to various production needs, making it suitable for both small-scale farms and large industrial facilities.
• Customization: The AI-driven module enables specific customization of growth parameters, enhancing the value of the biomass produced for specific industrial applications.

The system supports a wide range of applications:
• Biofuels: High-lipid algae strains cultivated in the system can be processed into biodiesel, offering a renewable alternative to fossil fuels.
• Bio plastics and Polymers: Algae can serve as feedstock for biodegradable plastics, reducing dependence on petroleum-based plastics.
• Animal Feed: Protein-rich algae can be used in aquaculture and livestock feed, providing a sustainable protein source.
• Carbon Capture and Utilization (CCU): The system can be integrated with industrial CO₂ emissions to create a closed-loop carbon capture and utilization process.

Tests and Results
The system was evaluated according to several algae cultivation and carbon capture standards. Tests on growth rate, CO₂ absorption efficiency, biomass yield, and energy consumption were conducted in controlled environments. Initial testing showed that the helix-shaped reactor design increased light penetration by 20-30% compared to flat-panel designs, resulting in up to a 40% higher growth rate. CO₂ capture efficiency exceeded 80% in simulations with industrial CO₂ sources, demonstrating the system's ability to support carbon-negative operations effectively.

AI module tests revealed a 25% improvement in resource utilization, as the real-time adjustments optimized light, nutrient, and CO₂ conditions across growth phases. Furthermore, the closed-loop water recycling system reduced water consumption by 50%, highlighting the system's sustainability for large-scale operations. These results underscore the system's advantages in terms of productivity, efficiency, and environmental impact, positioning it as a viable solution for sustainable algae cultivation across industries.
, Claims:5. CLAIMS
I/We Claim:
1. An AI-driven photo bioreactor system (100) for cultivating algae, comprising:
a. a reactor (102) configured to promote light penetration and carbon dioxide (CO₂) absorption for algae growth;
b. an environmental control module (104) configured to monitor and optimize environmental conditions, including light intensity, nutrient levels, temperature, pH, and CO₂ concentration to enhance algae growth;
c. a CO₂ capture module (106) integrated with the reactor to channel CO₂ emissions for algae absorption;
d. a modular structure (108) designed to allow reactors to be connected in parallel for scalable production;
e. an LED lighting system (110) providing adjustable light exposure to enhance photosynthesis and algae biomass yield;
f. a closed-loop system (112) configured to reduce water loss during algae cultivation for sustainable operation;
g. a data storage system (114) enabling real-time tracking of growth conditions for remote monitoring and control;
Characterized in that,
h. a helix-shaped reactor (102) maximizing surface area for light penetration and CO₂ diffusion, thereby enhancing photosynthetic efficiency and algae growth across the culture;
i. an AI module (104) utilizing machine learning algorithms to analyse real-time sensor data on key parameters, dynamically adjusting these conditions to optimize growth based on specific algae strains and production goals;
j. an integrated CO₂ capture module (106) that channels CO₂ emissions from industrial sources into the reactor, creating a carbon-negative process by converting CO₂ into algae biomass;
k. a modular structure (108) allowing flexible scalability, enabling multiple reactors to connect in parallel or in stacked configurations to meet varying production needs.

2. The system as claimed in claim 1, wherein the AI module (104) applies machine learning algorithms to continuously analyse data from sensors monitoring essential environmental factors, including dissolved oxygen and nutrient concentration, to optimize growth conditions in real time.

3. The system as claimed in claim 1, wherein the CO₂ capture module (106) is configured to source CO₂ from external industrial facilities, such as power plants and factories, and directly injects it into the reactor (102), thereby supporting a carbon-negative cultivation process.

4. The system as claimed in claim 1, wherein the helix-shaped reactor (102) includes a spiral tube arrangement that maximizes surface area for enhanced light exposure and CO₂ diffusion, thereby significantly improving photosynthetic efficiency for algae growth.

5. The system as claimed in claim 1, wherein the LED lighting system (110) is programmable to simulate natural day-night light cycles or customized light patterns tailored to algae growth requirements, with adjustable wavelengths suitable for different growth stages.

6. The system as claimed in claim 1, wherein the AI module (104) is further configured to select specific algae strains optimized for various applications, such as biofuel production, bio plastic feedstock, or animal feed, based on targeted lipid, carbohydrate, or protein profiles.

7. The system (100) as claimed in claim 1, wherein the closed-loop system (112) incorporates a water recycling mechanism that captures evaporated water, condenses it, and recirculates it into the reactor (102), minimizing overall water usage throughout the algae cultivation process.
8. The system (100) as claimed in claim 1, wherein the modular structure (108) is designed for scalable expansion, allowing for horizontal or vertical connection of multiple reactors (102) to flexibly increase production capacity based on specific operational requirements.

9. The system (100) as claimed in claim 1, wherein the data storage system (114) is cloud-based, enabling operators to remotely monitor, analyse, and control growth parameters via an accessible user interface on both computers and mobile devices.

10. A method for cultivating algae using the system as claimed in claim 1, comprising the steps of:
preparing the helix-shaped reactor (102) and introducing a selected algae strain according to the intended application;
setting initial growth parameters, including light intensity, temperature, and nutrient concentration, using the AI module (104);
monitoring and adjusting real-time environmental conditions through the AI module (104), wherein the AI module employs machine learning algorithms to interpret data from sensors monitoring light intensity, nutrient levels, temperature, and CO₂ concentration, thereby automatically optimizing conditions to maximize biomass yield.

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