Method and system for managing industrial processes

Abstract

A method for managing an industrial process based on a dynamically updated digital twin comprises: Creating a digital twin of the industrial process based on information pertaining to the industrial p

Description

TECHNICAL FIELD This invention relates to digital twins. In particular, though not exclusively, this invention relates to a method for managing an industrial process and a system for managing an industrial process. BACKGROUND With a boom in technological growth and population, various industries have sprouted over the world to meet growing demands of the consumers. Due to this surge in development of industries, there were initially discrepancies with respect to how industrial processes were carried out. In order to streamline such processes and standardise functioning of the industries, the industrial processes have been implemented. Each industry having its own specialisation generally has its own set of industrial processes which are utilised therein. Such industrial processes are often used for all industries including mechanical, electronic, chemical, manufacturing, packaging, oil and gas, and so forth. However, often due to small developments within the industrial process, or wear-and-tear of physical machinery, or other such parameters, such industrial processes get derailed. In most cases, this leads to a wastage of raw materials, damage of machinery, and so forth. Such cases not only lead to wastage of economies which would otherwise be used elsewhere but are also unsustainable for the environment. For example, oil pipes on a sea floor bursting not only increase costs of rebuilding the same, but also result in deaths of aquatic life. Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with managing industrial processes. SUMMARY OF THE INVENTION The present disclosure seeks to provide a method and a system that employs a pre-trained predictive domain model functioning based on sensor data and weighted combination of contributions from one or more operations of one or more apparatuses of one or more assets that are configured to collectively implement an industrial process, to enhance efficiency of the industrial process. An aim of the present disclosure is achieved by a method and a system for managing an industrial process as defined in the appended independent claims to which reference is made to. Advantageous features are set out in the appended dependent claims. In a first aspect, an embodiment of the present disclosure provides a method for managing an industrial process, the method comprising: obtaining information pertaining to the industrial process; creating at least one digital twin of the industrial process based at least on the information pertaining to the industrial process, wherein the at least one digital twin is dynamically updated in response to one or more changes to at least one asset, the asset comprised by the industrial process while the industrial process is executed; receiving a first set of input data from at least one data source, wherein the first set of input data is indicative of at least one parameter in the industrial process, and wherein the at least one data source generates control signals for controlling the industrial process in realtime; assigning weights to the first set of input data, wherein the weights are indicative of contributions from one or more operations associated with the industrial process based on a pre-defined priority thereof to the industrial process; predicting a first set of output data by executing at least one predictive domain model that is pre-trained, wherein the first set of output data is predicted based on the first set of input data, weighted combination of contributions from one or more operations associated with the industrial process, and the at least one digital twin; executing the at least one predictive domain model that is pretrained, using a mathematical addition or a mean of the first set of output data and the first set of input data, to generate a second set of input data, wherein the second set of input data is an accurate representation of the at least one predicted state of the industrial process; repeating the above steps for the at least one predictive domain model with updated values of the first set of input data until an acceptable value of a confidence factor is achieved; determining at least one change required in the industrial process, based on the at least one predicted state and at least one required state of the industrial process; and initiating, in response to the second set of input data, at least one process action to implement the at least one change, for optimising the industrial process. In a second aspect, an embodiment of the present disclosure provides a system for managing an industrial process, the system comprising at least one data source and at least one processor, wherein the at least one data source and the at least one processor are communicably coupled, and wherein the at least one processor is configured to: obtain information pertaining to the industrial process; create at least one digital twin of the industrial process based at least on the information pertaining to the industrial process, wherein the at least one digital twin is dynamically updated in response to one or more changes to at least one asset, the asset comprised by the industrial process while the industrial process is executed; receive a first set of input data from the at least one data source, wherein the first set of input data is indicative of at least one parameter in the industrial process; assigning weights to the first set of input data, wherein the weights are indicative of contributions from one or more operations associated with the industrial process based on a pre-defined priority thereof to the industrial process; predict a first set of output data by executing at least one predictive domain model that is pre-trained, wherein the first set of output data is predicted based on the first set of input data, weighted combination of contributions from one or more operations associated with the industrial process, and the at least one digital twin; executing the at least one predictive domain model that is pretrained, using a mathematical addition or a mean of the first set of output data and the first set of input data, to generate a second set of input data, wherein the second set of input data is an accurate representation of the at least one predicted state of the industrial process; repeating the above steps for the at least one predictive domain model with updated values of the first set of input data until an acceptable value of a confidence factor is achieved; determine at least one change required in the industrial process, based on the at least one predicted state and at least one required state of the industrial process; and initiate, in response to the second set of input data, at least one process action to implement the at least one change, for optimising the industrial process. Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments construed in conjunction with the appended claims that follow. It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS One or more embodiments of the invention will now be described, by way of example only, with reference to the following diagrams wherein: Figure 1 is a process flow depicting steps of a method for managing an industrial process, in accordance with an embodiment of the present disclosure; Figure 2 is a block diagram representing a system for managing an industrial process, in accordance with an embodiment of the present disclosure; and Figure 3 is an exemplary process flow depicting steps of a method for managing an industrial process, in accordance with another embodiment of the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible. In a first aspect, an embodiment of the present disclosure provides a method for managing an industrial process, the method comprising: obtaining information pertaining to the industrial process; creating at least one digital twin of the industrial process based at least on the information pertaining to the industrial process, wherein the at least one digital twin is dynamically updated in response to one or more changes to at least one asset, the asset comprised by the industrial process while the industrial process is executed; receiving a first set of input data from at least one data source, wherein the first set of input data is indicative of at least one parameter in the industrial process, and wherein the at least one data source generates control signals for controlling the industrial process in realtime; assigning weights to the first set of input data, wherein the weights are indicative of contributions from one or more operations associated with the industrial process based on a pre-defined priority thereof to the industrial process; predicting a first set of output data by executing at least one predictive domain model that is pre-trained, wherein the first set of output data is predicted based on the first set of input data, weighted combination of contributions from one or more operations associated with the industrial process, and the at least one digital twin; executing the at least one predictive domain model that is pretrained, using a mathematical addition or a mean of the first set of output data and the first set of input data, to generate a second set of input data, wherein the second set of input data is an accurate representation of the at least one predicted state of the industrial process; repeating the above steps for the at least one predictive domain model with updated values of the first set of input data until an acceptable value of a confidence factor is achieved; determining at least one change required in the industrial process, based on the at least one predicted state and at least one required state of the industrial process; and initiating, in response to the second set of input data, at least one process action to implement the at least one change, for optimising the industrial process. In a second aspect, an embodiment of the present disclosure provides a system for managing an industrial process, the system comprising at least one data source and at least one processor, wherein the at least one data source and the at least one processor are communicably coupled, and wherein the at least one processor is configured to: obtain information pertaining to the industrial process; create at least one digital twin of the industrial process based at least on the information pertaining to the industrial process, wherein the at least one digital twin is dynamically updated in response to one or more changes to at least one asset, the asset comprised by the industrial process while the industrial process is executed; receive a first set of input data from the at least one data source, wherein the first set of input data is indicative of at least one parameter in the industrial process; assigning weights to the first set of input data, wherein the weights are indicative of contributions from one or more operations associated with the industrial process based on a pre-defined priority thereof to the industrial process; predict a first set of output data by executing at least one predictive domain model that is pre-trained, wherein the first set of output data is predicted based on the first set of input data, weighted combination of contributions from one or more operations associated with the industrial process, and the at least one digital twin; executing the at least one predictive domain model that is pretrained, using a mathematical addition or a mean of the first set of output data and the first set of input data, to generate a second set of input data, wherein the second set of input data is an accurate representation of the at least one predicted state of the industrial process; repeating the above steps for the at least one predictive domain model with updated values of the first set of input data until an acceptable value of a confidence factor is achieved; determine at least one change required in the industrial process, based on the at least one predicted state and at least one required state of the industrial process; and initiate, in response to the second set of input data, at least one process action to implement the at least one change, for optimising the industrial process. Throughout the present disclosure, the term "industrialprocess" refers to a process carried out in an industry. Such processes may be implemented for producing goods or providing services. Examples of the industrial process include, but are not limited to, a mechanical process, a chemical process, a mining process, an automation process, a manufacturing process, a printing process, a packaging process. Moreover, herein the term 'managing' pertains to all aspects of managing the industrial process, for example, observing the industrial process, detecting possible issues or drawbacks in the industrial process, finding solutions for the industrial process, optimising the solutions for the industrial process, and so forth. The term "information pertaining to the industrial process" refers to data indicative of various aspects of the industrial process. Notably, such information provides an insight into the industrial process. Optionally, such information is obtained using at least one sensor. Alternatively, optionally, such information is historical information from previously implemented industrial processes. In this case, the information is obtained from an external device, a database, and the like. Optionally, the information pertaining to the industrial process comprises at least one of: information pertaining to machinery involved for the industrial process, information pertaining to structure of the machinery, information pertaining to specifications of the machinery, information pertaining to raw materials, information pertaining to steps being followed for the industrial process. Throughout the present disclosure, the term "digital twin" refers to a virtual representation of a real-world asset, which serves as a real-time digital counterpart. This means that, the at least one digital twin is updated in real-time during its entire lifecycle, depending on changes observed in the at least one real-world asset. Moreover, the at least one digital twin utilises at least simulation, machine learning and reasoning technologies for assisting in decision-making. Herein, the real-world asset refers to an object or a process which exists in a real-world. The at least one digital twin is created using the information pertaining to the industrial process. As previously mentioned, such information provides insights into the industrial process. It will be appreciated that the at least one digital twin is a virtual counterpart of the industrial process. Notably, the at least one digital twin is dynamically updated, which means that any changes being observed in the industrial process are updated in the at least one digital twin while the industrial process is being updated. The at least one digital twin being dynamically updated allows possible shortcomings in the industrial process to be identified before significant damage occurs, and thereby be resolved. Updating of the at least one digital twin comprises removing existing data values, and / or replacing the existing data values with new (i.e., updated) data values. It will be appreciated that such updating ensures that the at least one digital twin is a real-time virtual replica of the industrial process, since the at least one digital twin is constantly updated depending on changes of the industrial process. This allows functions to be performed on the at least one digital twin virtually, for improving performance and accuracy. Herein, the term "asset" refers to a physical or tangible item that is utilised in the production or operation of a facility established to implement a given industrial process. Examples of the facility include, but may not be limited to, micro-fabrication plants, manufacturing plants, steel mills, water treatment works, recovery assembly factories, power stations, oil and gas fields, quarries, mines, in-situ mining plants, water utilities, foundries, steel industry, petrochemicals industry, nuclear industry, transport facilities, water treatment works and food processing facilities. The facilities may include multiple assets having plurality of data sources to collect and monitor the one or more parameters associated with various assets. Assets can include machinery, equipment, tools, vehicles, buildings, infrastructure, and even intellectual property such as patents or proprietary technology. However, the asset has different apparatus working in conjunction with each other at different operating conditions which often makes the asset part of an overall system. So, each of the apparatus may deviate from its optimised efficiency if it were to work in a standalone mode of operation. Examples of the asset include, but are not limited to, a mining facility employing an array of bore holes with submersible pumps in which water or other fluid is flushed in ground between the bore holes to flush out particles of matter, for example rare-earth elements, uranium particles, thorium particles, a manufacturing facility such as a power generating facility. In another example, the asset is a sub-section of a foundry. The subsection of foundry optionally includes multiple apparatuses which are monitored via different types of data sources. Examples of these multiple apparatuses include, but are not limited to, pumps, fans, compressors, rock crushers, screens, transporter belts, hoppers, cooling towers, HV AC and furnaces. The at least one data source is optionally adjusted to monitor at given intervals for collection of appropriate amounts of first set of input data. The assets are essential for carrying out the necessary tasks and functions within the industrial process, and they contribute to the overall efficiency, productivity, and profitability of the operation. Proper maintenance and management of assets are crucial to ensure optimal performance and longevity, which can ultimately impact the success of the industrial process. Notably, the digital twin provides control and automatisation of different assets of the industrial process and reduces chances of accidents from occurring. In addition, the automatisation of the assets helps to reduce energy consumption and improves asset condition in the given facility. In an example, optimisation of the assets can offer energy improvements of up to more than ca 5%, preferably more than ca 15% and most preferably more than ca 25% of the overall energy consumption of an overall system. Similarly, the savings in for example water consumption in mining installations may be reduced by more than ca 2%, preferably ca 6% and most preferably ca 10%. Optionally, the at least one digital twin may not be updated in real-time. Herein, a process of the at least one digital twin may be slowed down to provide an output at desired time intervals, such that information may be derived from the at least one digital twin. In this manner, small changes may be captured which assist in optimising the industrial process. Moreover, the process of the at least one digital twin may be accelerated to anticipate behaviour and performance. The term "input data" refers to a collection of data or information which is used as an input for the at least one predictive domain model. The first set of input data is used as an input for predicting the first set of output data using the at least one digital twin. The input data is optionally expressed as numerical values indicative of the at least one parameter at a given time instant. Optionally, the input data is presented in a tabular pattern, wherein each column corresponds to a given parameter and each row corresponds to a given time instant. Optionally, the input data is predictive of at least one state of the industrial process. Optionally, the input data comprises at least one of: historic data, realtime data, of the industrial process. The historic data refers to collected data pertaining to past events that have transpired. For example, the historic data may comprise data pertaining to a drill for a mining industrial process. Optionally, the historic data is used for training the at least one predictive domain model for managing the industrial process. Beneficially, such historic data provides information regarding how the industrial process is conducted and issues that may have occurred in the past. Moreover, using such data allows the at least one predictive domain model to learn possible scenarios from historical occurrences. The realtime data refers to collected data pertaining to instantaneous changes in the industrial process. Such real-time data is instantaneously available as soon as it is created and / or acquired. Optionally, the real-time data is used for managing the industrial process. Beneficially, such real-time data provides information pertaining to the execution of the industrial process, such that if there are any discrepancies, the at least one predictive domain model can identify the same and suggest appropriate changes required. The term "data source" refers to a memory which is configured to store at least the input data. The data source monitors and collects the data corresponding to the status / operating conditions of the plurality of apparatus of the asset in real-time and transmits the data in real-time in a form of signals to the at least one processor that is configured to process and analyse the obtained first set of input data from the at least one data source. Optionally, the at least one data source is implemented as a virtual sensor. The virtual sensor is a sensor which is virtually deployed to sense the at least one parameter in the industrial process. Such virtual sensors are trained using historical data to sense the at least one parameter and are not placed physically within a system. Optionally, such virtual sensors are built using principles of physics and chemistry. It will be appreciated that the virtual sensor is often a result of a model. Optionally, the at least one data source is implemented as at least one of: a physical sensor, a device. In some cases, deploying physical sensors (such as, for example, a flow sensor) is often avoided due to high costs involved. In such cases, virtual sensors are utilised, which assist in optimising processes as well since they are built using the model. Herein, the model calculates a series of criteria (i.e., parameters), pertaining to a given process (such as, for example, temperature, pressure, flow, and so forth). In an example, the industrial process involves a set of interconnected floatation cells, wherein some parameters between individual floatation cells are not physically measured, and merely an intake of a first floatation cell and an output of a last floatation cell are measured. In such a case, these parameters are generated using virtual sensors for optimising the industrial process. Moreover, virtual sensors enable the extraction of information related to the industrial process without relying on physical sensors, thereby reducing the cost and complexity associated with deploying and maintaining an extensive array of hardware sensors. The creation of digital twins involves replicating the industrial process in a virtual environment, facilitating a dynamic, real-time representation of the ongoing process. Advantageously, virtual sensors are more capable for real-time monitoring and control, dynamically updating digital twins as the industrial process unfolds, provide an accurate and up-to-date reflection of the actual process conditions. This real-time insight allows for prompt identification of deviations, optimisation of parameters, and proactive decision-making. Furthermore, virtual sensors can combine data from multiple sources, including physical sensors, historical data, and models, to estimate process variables that may be difficult or expensive to measure directly. By leveraging advanced data fusion and estimation techniques, virtual sensors can provide accurate estimates of critical process variables. Virtual sensors can also be used to detect anomalies or faults in the process by comparing the estimated values with the actual measurements from physical sensors. This can help in early detection of faults, reducing downtime, and improving overall process reliability. Beneficially, since virtual sensors do not have physical components that can degrade over time, they require less maintenance compared to physical sensors. Moreover, virtual sensors can significantly reduce the cost associated with installing physical sensors. They eliminate the need for purchasing, installing, calibrating, and maintaining physical sensors, which can be expensive, especially in large-scale industrial processes. Therefore, using virtual sensors as a source for input data in creating a digital twin of an industrial process offers benefits such as costeffectiveness, flexibility, improved accuracy, fault detection, process optimisation, reduced maintenance downtime, and enhanced data fusion and integration capabilities. The physical sensor is an electromechanical device which senses the at least one parameter in the industrial process. Such physical sensors are deployed by being physically placed within the industrial process. Examples of the physical sensor include, but are not limited to, a pressure sensor, a motion sensor, a light sensor, a flow sensor, a temperature sensor, an optical sensor, a magnetic sensor, a proximity sensor, an infrared sensor, a level sensor. The device is an electromechanical device capable of capturing, storing and / or sharing the input data. Herein, the device may be implemented as: a device of the industrial process, an external device, a user device. The device of the industrial process is a device embedded within the industrial process, the external device is a device which is not embedded within the industrial process but provides some insight into the industrial process, and the user device is a device associated with a user and which is capable of capturing, storing, and sharing data. Beneficially, the above-mentioned implementations of the at least one data source provide valuable insights into the industrial process. Optionally, the at least one data source is applied to one or more apparatus of an asset configured to perform the one or more operations associated with the industrial process. Typically, at least one data source, pre-programmed controllers and a set of actuators are installed on and / or near different apparatus of an asset in a facility. A large amount of data is collected at substantially real-time or at pre-defined intervals from such at least one data source and intelligent responses, namely processed data, are generated from the data collected from various assets of an industrial process to increase the efficiency of the asset. It may be appreciated that the at least one data source is provided in a portable, interchangeable, mobile kit. For example, a mobile kit for profiling well pump performance in the mining industries includes a flow rate meter and a water level sensor in the multiple sensors. Data from the sensors are collected and logged locally and then in real-time or at selected intervals transferred to the at least one processor via network connections, network (cellular) operators or a mobile data storage device such as a smartphone, tablet or phablet computer. It may be appreciated that the at least one data source applied to one or more apparatus of an asset may be configured to obtain the first set of input data therefrom from a remote location, such as virtually, or alternatively at the site close to the asset itself. Optionally, the method further comprises detecting the one or more apparatus of the asset to which the at least one data source is applied, for determining whether the one or more apparatus of the asset is operating correctly. In this regard, the data obtained from the at least one data source is compared with predefined thresholds or benchmarks to determine whether the apparatus are within acceptable operating parameters. Anomalies or deviations from normal behaviour indicative of potential issues or malfunctions are identified by employing machine learning or anomaly detection algorithms and assess the overall health of the asset. Moreover, upon determination of whether the apparatus are operating correctly, the method facilitates diagnostic procedures and decision-making processes, such as diagnostic insights or recommendations and generating alerts or notifications for maintenance personnel or operators when abnormalities are detected, prompting troubleshooting and resolving issues and further investigation or intervention. Moreover, determining whether the one or more apparatus of the asset is operating correctly, enables supporting decision-making regarding maintenance scheduling, resource allocation, or operational adjustments based on the condition of the asset. Beneficially, incorporating the detection of apparatus status and performance into the method, organisations enables proactive monitoring and managing the health of the assets, helping to prevent downtime, minimising disruptions, and maximising operational effectiveness within the industrial process. Moreover, the at least one data source generates control signals for controlling the industrial process in real-time. The term "control signal" refers to a signal which represents a control command for controlling the industrial process. Optionally, the industrial process is controlled in near real-time. In such cases when real-time data cannot be instantaneously implemented using the at least one predictive domain model, the control signals for controlling the industrial process are generated by the at least one data source in near real-time. Throughout the present disclosure, the term "parameter" refers to an element of the industrial process, which is useful for identifying the industrial process, or evaluating at least a performance, a status, or condition of the industrial process. Examples of the at least one parameter include, but are not limited to, a pressure parameter, a motion parameter, a light parameter, a flow parameter, a temperature parameter, an optical parameter, a magnetic parameter, a proximity parameter, an infrared parameter, a level parameter. Notably, such first set of input data is received such that corresponding first set of output data, and thereafter the second set of input data may be predicted using the at least one predictive domain model to determine the at least one change. In an example, the at least one processor analyse the various parameters associated with the at least one apparatus, such as pump, fan, compressors, cooling tower, HVAC and furnace of the asset. Examples of various parameters include, but are not limited to, a combination and association of temperature, pressure, humidity, working conditions, and peak values pertaining to different operating conditions. The at least one processor are provided with simulation models namely digital twin component of the one or more apparatus of the asset to which the at least one data source is applied. The simulation models are employed for identifying adjustments that improve the efficiency of operation of the asset. The term "weights" as used herein refers to numerical values assigned to different elements or variables, such as an operation of an asset (or an apparatus thereof) to indicate their relative importance or contribution within a system or process, such as for managing an industrial process. Notably, the weights are used to prioritise or emphasise certain factors over others when making decisions or performing calculations. For example, in the disclosed method, the weights are assigned to a first set of input data based on the priority of associated operations in an industrial process, the weights indicate the significance of each input data from amongst a first set of input data in relation to achieving the desired outcomes of the industrial process. Optionally, the weights may be assigned based on various criteria, such as expert judgment, historical input data analysis, statistical models, stakeholder input, industry standards, regulatory requirements, or optimisation algorithms. Optionally, the weights may be normalised to ensure that their sum equals one or to facilitate comparison between different variables. Notably, higher weights indicate greater importance or contribution, while lower weights signify lesser significance. Beneficially, by incorporating weights into data analysis and decision-making processes, organisations can focus their efforts on the most critical factors and improve the overall effectiveness and efficiency of their operations. In this regard, the method identifies various operations involved in the industrial process. Optionally, such operations include, but do not limit to, manufacturing steps, quality control procedures, maintenance activities, logistics, and more, wherein each operation plays a specific role in the overall industrial process and contributes to its success. Once the operations are identified, the method comprises determining the priority of each operation based on factors such as criticality (for example, a quality control operation might be prioritised higher than a routine maintenance task), efficiency, safety, cost-effectiveness, and so forth. With priorities established for each operation, weights are assigned to each input data from amongst the first set of input data, based on the pre-defined priorities. Input data could include various parameters, measurements, or variables that influence the performance or outcome of the industrial process. For example, if quality control is identified as a high-priority operation, input data related to product quality metrics may be assigned higher weights. Similarly, if safety is a top priority, input data related to safety incidents or compliance measures may be given greater weight. Once the input data is weighted, it can be used in various ways to optimise the industrial process, such as for decision making (e.g., resource allocation, scheduling, or process adjustments), process optimisation, continuous improvement, and so forth. Beneficially, assigning weights to input data based on operation priorities helps ensure that resources are allocated effectively, critical aspects of the process receive appropriate attention, and decisions are aligned with overarching goals and objectives of the industrial process. Optionally, the weighted combination of contributions from one or more operations associated with the industrial process is computed using one or more weighting factors determined from at least one of: an analysis of a historical first set of input data for determining a set of values for the one or more weighting factors which enable the aggregate efficiency to be most representative of the one or more operations associated with the industrial process; and an application of operating perturbations to the one or more operations and utilising a corresponding detected change in the aggregate efficiency for iterating the set of values for the one or more weighting factors, for optimising the industrial process. In this regard, the weighted combination is computed via use of one or more weighting factors. The one or more weighting factors are calculated using an analysis of historical first set of input data from the at least one data source for determining a set of values for the one or more weighting factors which enable the aggregate efficiency to be most representative of operation of the asset. In addition, the one or more weighting factor are determined by using an application of operating perturbations to operating conditions of the asset and utilising a corresponding detected change in the aggregate efficiency for iterating values of the one or more weighting factors for enabling the operating efficiency of the asset to be improved. Optionally, the analysis of a historical first set of input data employs at least one of: artificial intelligence, neural network analysis for determining the set of values for the one or more weighting factors. The analysis can utilise different approaches for determining the one or more weighting factors. Notably, for determining the one or more weighting factors, the analysis utilises artificial intelligence, neural network analysis or both. The overall efficiency is calculated by considering mutually interaction of the at least one apparatus under different operating conditions. In addition, some of the weighting factors (wf) are employed to compute the aggregate efficiency (Eagg). The weighting factors are determined by analysis of historical first set of input data, performing a sensitivity analysis by applying small perturbations to operating setting of the asset in real-time and the like. The aggregate assessment of operating efficiency Eagg which is computed, for example, from a weighted summation of individual efficiencies of the at least one apparatus, as defined by Equation 1 (Eq. 1): Eagg = 2^=1 wfjEj Eq. 1 wherein Ej = efficiency of a given apparatus with index i; wfj = weighting factor of efficiency for apparatus i; n = a total number of apparatuses being optimised The aggregate assessment of operating efficiency Eagg provides an overall indication of an operating efficiency of a given facility. However, the multiple apparatuses are mutually interconnected and interact, such that an adjustment to an operating parameter for one given apparatus to change its efficiency, for example a change in operating pressure of a pump, will influence efficiencies of other apparatus. Thus, both the weighting factors wfj and the efficiencies of the apparatus Ej are functions of operating parameters of the apparatus, for example as measured by the aforesaid data sources, namely virtual or physical sensors or devices, and determined from one or more set-points applied to control the apparatus. Moreover, for correct and safe functioning of the facility, there will be certain ranges of permissible values for the sensor signals and the set-points, for example for ensuring that the facility runs safely and / or processes implemented in real-time in the facility function to required quality and / or productivity criteria. By monitoring the apparatus, via data derived from sensor signals, the system is able to compute interrelationship between the apparatus, for example via employing simulation models, for example via tables of apparatus operating characteristics, for computing the weighting factors wfj. For example, the interactions between the apparatus are optionally determined by applying small test perturbations to operating parameters of the apparatus and then monitoring a responsive behaviour of the apparatus. The weighting factors wfj are then computed so that aggregate assessment of operating efficiency Eagg provide a representative indication of a general operating efficiency of the facility, and the weighting factors wfj provided insight regarding one or more critical apparatus of the facility which have a major influence on the aggregate efficiency Eagg and which need to be monitored and adjusted especially diligently. Further, the embodiment of the disclosure may also utilise the substantially real time data collected to be analysed for optimising the one or more assets and overall system in non-real time. The post data collection analysis where adjustments of operating parameters are introduced in non-real time in the overall system allows for gradual introduction of changes. This reduces the complexity of the controlling of the overall system and also allows careful analysis of the cost implications of changed operating conditions to be weighed up against problems in performance or operation due to the changed conditions. If adjusting some operating parameters of one or more assets can save $50,000 but the risk of getting it wrong could damage $5 Million in production costs then further analysis or no adjustment would be performed. Determining aforesaid interrelationships between the apparatus of the facility is beneficially implemented using matrix representations of sensor signals and facility set-points, wherein matrix-solving computer program tools are employed to solve a large multitude of multivariable simultaneous equations represented by such matrices. Such matrixsolving tools are beneficially employed in the one or more cloud computing resources whereat distributed array processors are available which are especially well adapted for matrix manipulation and associated solving. In an embodiment, the method employs receiving a first set of input data from the virtual sensors applied to at least one apparatus of the at least one asset of the facility to implement a given industrial process to collect data or monitor at least one parameter in the industrial process in realtime; and assigning weights to the first set of input data, wherein the weights are indicative of contributions from one or more operations of the at least one apparatus of the at least one asset of the facility associated with the industrial process based on a pre-defined priority thereof to the industrial process. Beneficially, the synergistic combination of virtual sensors (and optionally physical sensors), along with weighted data aggregation, offers numerous benefits including enhanced data accuracy, redundancy and fault tolerance, adaptability and flexibility for more agile responses to evolving requirements or conditions of the industrial process, and cost-efficiency. Additionally, beneficially, by combining data from multiple data sources, namely, sensors, and applying appropriate weights to the sensor data, based on their reliability and relevance, more comprehensive analyses can be performed, leading to deeper understanding for improved decision-making capabilities and optimisation of the industrial process. For example, more informed decisions can be made regarding process control, maintenance schedules, and resource allocation, ultimately leading to improved efficiency and productivity. Optionally, the method further comprises identifying adjustments that improve efficiency of one or more operations associated with the industrial process. Notably, the simulation models are employed for identifying adjustments that improve the efficiency of operation of the asset. Upon identification of the various operations associated with the industrial process and assignment of corresponding weights to the first set of input data based on the priority of associated operations, simulation models employ mathematical algorithms and computer simulations to simulate the behaviour of the industrial process and the operation of assets within it to predict how changes to the process or asset operation might impact efficiency. Notably, the simulation models allow for a detailed analysis of efficiency by simulating various scenarios to explore the impact of potential adjustments on efficiency, identify the configuration or settings of assets that lead to the highest efficiency, how changes to operations or asset parameters will affect overall process performance. Based on the analysis conducted with simulation models, adjustments to improve efficiency are identified. Such adjustments may include fine-tuning operational parameters (such as speed, temperature, or pressure) to optimise asset performance, modifying maintenance schedules to minimise downtime without sacrificing asset performance, redesigning workflows or resource allocation to streamline processes and reduce bottlenecks, implementing and validating adjustments through further simulation or real-world testing, ongoing monitoring of efficiency and continuous improvement efforts based on the results obtained from simulation models and real-world performance data. Beneficially, this enhances the accuracy and effectiveness of optimisation efforts, ultimately leading to improved performance and productivity. Optionally, the method further comprises providing a condition-based maintenance plan for the one or more assets. Optionally, the method comprises designing an optimum maintenance schedule that is linked to the one or more apparatus and one or more individual asset and further the overall system performance and efficiency. Currently most maintenance schedules are done based on the schedule of the maintenance team and not linked to the equipment condition. A condition based preventive and predictive maintenance process, which utilises the collected data from the one or more assets, or the overall system may be used to improve on the life of apparatus and components or wear parts of the assets in the overall system. Based on real time tracking of the system through wireless sensors and asset efficiency, a baseline efficiency is calculated which is used as a trigger to identify the typical maintenance cycle. If the performance of the asset drops below the baseline at a given instance or for extended time during an analysed period notifications are sent to the system for actions to be initiated to improve on the maintenance schedule. Tolerances of the base line may be set for different sensitivity depending on the type of asset like a pump, compressor, furnace, cooling tower, rock crusher, transporter belt, material screens, or other suitable apparatus. This cycle is then used to predict future maintenance cycles of the system and asset saving time, cost and resources. Further, the improved maintenance schedule may also be linked in with Enterprise Resource Planning (ERP) systems of the manufacturing plant or other installations to optimise the overall efficiency. For example, in the use in an in-situ mining process the maintenance of the well and a submersible pump is scheduled by BRAINS.APP by processing substantially real time data of the flow rates and power consumptions of the pump. For example, a time series analysis model is employed based on the principle of a Kalman filter in order to estimate "true" state of the pump on the basis on an incoming noisy measurement from the sensors. A prediction is then made about optimal maintenance cycle that provides a stable pump output and keeps the production within the target interval. Optionally, prior to the step of predicting the first set of output data, the method comprises: creating the at least one predictive domain model using at least one machine learning algorithm; and training the at least one predictive domain model using the first set of input data and the weighted combination of contributions from one or more operations associated with the industrial process. Throughout the present disclosure, the term "predictive domain model" refers to a model which predicts future events or outcomes in the industrial process by analysing patterns using the at least one digital twin of the industrial process and the first set of input data. Optionally, a predictive domain model can be implemented for simulating a portion or the whole of an industrial process. Examples of the at least one predictive domain model include, but are not limited to, a classification model, a clustering model, a forecast model, an outliers model, a time series model. Notably, the at least one predictive domain model is created using the at least one machine learning algorithm. The term "machine learning algorithm" refers to an algorithm which creates the at least one predictive domain model, such that it is able to learn and predict outcomes without being explicitly trained to do so. Moreover, the at least one predictive domain model is thereby trained using the input data, which is labelled, allowing the at least one predictive domain model to learn and grow accurate over time. Examples of the at least one machine learning algorithm include, but are not limited to, a linear regression algorithm, a logistic regression algorithm, a decision tree algorithm, a support vector machine (SVM) algorithm, a Naive Bayes algorithm, a k-nearest neighbours (KNN) algorithm, a K-means algorithm, a random forest algorithm. Optionally, the predictive domain model is implemented using artificial intelligence. Optionally, the predictive domain model is based on a predictive algorithm. Examples of the predictive algorithm include, but are not limited to, a random forest algorithm, a generalised linear model (GLM) algorithm, a gradient boosted model (GBM) algorithm, a K-means algorithm, a prophet algorithm. Beneficially, the at least one predictive domain model is created and trained herein such that the at least one predictive domain model is pre-trained for use in optimising the industrial process. It will be appreciated that the at least one predictive domain model leverages the input data to optimise the industrial process. The term "output data" refers to data which is outputted from the at least one predictive domain model. It will be appreciated that the first set of output data is outputted from the at least one predictive domain model, and the first set of input data and the weighted combination of contributions from one or more operations associated with the industrial process are inputted into the at least one predictive domain model. Herein, the first set of output data is representative of the at least one predicted state of the industrial process based on the first set of input data and the weighted combination of contributions from one or more operations associated with the industrial process. It may be appreciated that an output is an outcome of an executed process and similarly the predicted output data obtained herein is the outcome of execution of the at least one predictive domain model. Moreover, the at least one predictive domain model is a pre-trained model. Furthermore, the at least one predictive domain model may include, but not be limited to, a standard industrial process model, a tested industrial operation model, and so forth. In an example, the at least one predictive domain model may include a machine learning model and / or an artificial intelligence model to pre-train the at least one predictive domain model, which predicts the first set of output data and the second set of input data based on the execution of the at least one predictive domain model. Furthermore, the prediction of the first set of output data is based on the first set of input data, the weighted combination of contributions from one or more operations associated with the industrial process and the at least one digital twin. It may be appreciated that the first set of input data provides a trigger and / or a throughput that simulates processing of the first set of output data. Herein the prediction of the first set of output data is a cumulative process of translating the first set of input data and the weighted combination of contributions from one or more operations associated with the industrial process to the at least one digital twin and executing the at least one predictive domain model in tandem. Throughout the present disclosure, the term "predicted state" refers to a state of the industrial process which is predicted based on the first set of input data and optionally the weighted combination of contributions from one or more operations associated with the industrial process. Furthermore, the at least one predicted state may be interpreted as a step of a conventional industrial process. Herein, the step of the conventional industrial process includes a portion of the step, a prior step which has already been executed, a future step which has to be executed. In an example, the at least one predicted state may include an increase in temperature of a heating process, a reduction in pressure in a compression process, and such like in the conventional industrial process. According to an embodiment of the present disclosure, the at least one predicted state is represented through the second set of input data, and the first set of output data of the industrial process. Based on an implementation with the present embodiment, the second set of input data is beneficially predicted prior to physical occurrence in the industrial process. Furthermore, the prediction is based on execution of the at least one predictive domain model and its representation using the second set of input data. Optionally, the at least one predictive domain model comprises at least one of: a material model, a ball charge model, a liner wear model, a dynamic charge model, inferential trajectory model, ball breakage model, new ball ejection model, a material processing model, a data processing model, a waste management model, a packaging model, an assembly model, a printing model. It will be appreciated that the at least one predictive domain model auto generates determining steps based at least one of: a given input data, a given data source, an ideal industrial method, a reoccurrence, a combination thereof. Furthermore, the at least one predictive domain model when executed, defines the predictive state of the industrial process. Notably, the addition of the first set of output data and the first set of input data is implemented as: a mathematical addition of respective values, a mean of respective values. For example, if the first set of output data has a value 4 and the first set of input data has a value 5, then the addition would either be 9 or 4.5, depending on if the addition is implemented as a mere addition or a mean. It will be appreciated that the second set of input data is outputted from the at least one predictive domain model, and the addition of the first set of input data and the first set of output data is inputted into the at least one predictive domain model. Herein, the second set of input data is the accurate representation of the at least one predicted state, which means that the second set of input data entails parameters in accordance with parameters during the at least one predicted state. It will be appreciated that the second set of input data is utilised to determine the at least one change with respect to the at least one predicted state. Moreover, the above-mentioned process for the at least one predictive domain model with updated values of the first set of input data is repeated until an acceptable value of a confidence factor is achieved. Optionally, the confidence factor is indicative of an accuracy of prediction and the acceptable value of the confidence factor is indicative of an efficient functioning of the industrial process. The confidence factor is indicative of an accuracy of prediction and / or function. The acceptable value of the confidence factor demonstrates efficient functioning of the industrial process. In this regard, it will be appreciated, that the at least one domain model is continually executed with updated values of the first set of input data until a required accuracy is achieved. Throughout the present disclosure, the term "at least one change" refers to an iteration in the industrial process, which results in a deviation of the overall industrial process. The term "required state" refers to a desired output considering the given change in the first set of input data that deviates an ongoing process to achieve the at least one required state. The at least one required state may indicate at least one of: an ideal state, a standard procedural state, a position, in the industrial process. Furthermore, the at least one required state may be referenced from a conventional industrial plan, or an established industrial data obtained from experimental setups and experimental data. Herein, the at least one predicted state and the at least one required state of the industrial process are mapped to find similarities and / or dissimilarities. Herein, the at least one change would be required to change the at least one predicted state to the at least one required state. This means that once the at least one change is implemented, the at least one predicted state and the at least one required state must be similar. When the at least one predicted state is similar to the at least one required state, the at least one predicted state will mimic the at least one required state, and provide outputs akin to a local sensor installed within the industrial process. For example, if the at least one predicted state is a temperature of 30 degrees and the at least one required state is a temperature of 100 degrees in a heating chamber, the at least one change may be to increase the heat within the heating chamber to reach 100 degrees. Optionally, when determining the at least one change required in the industrial process, the method is configured to: determining at least one proposed change based on the at least one predicted state and the at least one required state; simulating the at least one proposed change in the at least one digital twin of the industrial process to generate a simulated output state; assessing whether the simulated output state is similar to the at least one required state; and when the simulated output state is similar to the at least one required state, implementing the at least one proposed change as the at least one change required in the industrial process. The at least one proposed change refers to at least one change proposed by the at least one prediction domain model for changing the at least one predicted state to the at least one required state. The at least one proposed change is determined by mapping similarities and differences of the at least one predicted state and the at least one required state. In an example, the industrial process requiring a change in temperature by an increase of heat input in a heating chamber, amounts as at least one proposed change for the heating chamber of the industrial process. Thereon, the at least one proposed change is simulated in the at least one digital twin to anticipate an output state of the industrial process when the at least one proposed change is implemented. Since the at least one proposed change is simulated, it beneficially saves costs and effort while providing insights with respect to the at least one proposed change being simulated. The simulated output state is mapped with the at least one required state by comparing similarities and differences of the same. Notably, the at least one proposed change is implemented as the at least one change required in the industrial process only when the simulated output is similar to the at least one required state. It will be appreciated that such determination of the at least one change required in the industrial process is efficient, sustainable and saves excessive costs since it simulates the at least one proposed change in the at least one digital twin before applying the at least one change to the industrial process. Herein, if any issues are flagged during the simulation, those are sought out and the at least one proposed change is altered until it provides a desired result. Since the second set of input data is predictive of at least one state of the industrial process, it is utilised to determine the at least one predicted state. It will be appreciated that the at least one state is an actual state of the industrial process in real-time, whereas the at least one predicted state is based on the prediction of the second set of input data. The term "process action" refers to an action which is to be performed in real-time in the industrial process. In some cases where the industrial process is physically deployed, the at least one process action is a physical action. In other cases where the industrial process is virtually deployed, the at least one process action is a virtual action. Examples of the at least one process action include, but are not limited to, changing a temperature, changing a pressure, changing a material supply rate, of the industrial process. Beneficially, such method optimises the industrial method by efficiently determining the at least one change required in the industrial process and implementing the same using the at least one process action. Optionally, the at least one process action is initiated autonomously or semi-autonomously. Herein, when the at least one process action is initiated autonomously, no approvals or verifications are required from any entity, however, when the at least one process action is initiated semi-autonomously, an approval and / or verification is required from an entity. Optionally, the method is configured to employ adaptive data encryption and data obfuscation processing operations depending upon the at least one parameter of the industrial process. Herein, such adaptive data encryption and data obfuscation processing operations garner a degree of data protection to the method, making it safer against hacking attacks. Optionally, the data is being encrypted using any suitable method of data encryption. Optionally, data is randomised before encryption for increased security. By enhancing the degree of data protection, the method is less prone to being disrupted by malicious third parties, for example by injection of computer viruses or by selective eavesdropping and substitution of data flows. By employing data protection that is adaptively adjusted depending upon at least one parameter of the industrial process, the method can be both highly efficient in its operation and also highly robust to attack. Furthermore, the adaptive data encryption and data obfuscation advantageously make the method robust, and prevent it from unwanted intrusions, for example third-part malicious attacks. Moreover, adaptive data encryption employed by the given digital twin encrypts the data being exchanged based on the type of data. Moreover, such data includes a confidence factor of the given digital twin, the information pertaining to the industrial process, the first set of input data, the first set of output data, the second set of input data, the at least one change, and so forth. Optionally, the method further comprises sending a notification to at least one device associated with an entity, wherein the notification is indicative of the at least one change, and wherein the entity performs the at least one change to optimise the industrial process. Herein, the at least one change is being implemented semi-autonomously since the at least one predictive domain model is being utilised to determine the at least one change but the at least one change is eventually performed by the entity. The term "entity" refers to a physical entity capable of performing the at least one change. Optionally, the entity is implemented as at least one of: a person, a robot. The at least one device associated with the entity refers to a communication device capable of receiving and accessing the notification. Optionally, the notification is implemented as at least one of: a visual notification, an audio notification, a haptic notification. It will be appreciated that the notification is sent to the at least one device associated with the entity, such that the entity performs the at least one change. If such a notification is not provided in a timely manner, it may be detrimental to the industrial process. Thereby, the sending of such notification to the entity is beneficial since it allows timely resolution of issues in the industrial process and avoids unnecessary wastage or damage. Optionally, the method further comprises: calculating an uncertainty of the at least one process action for implementing the at least one change; comparing the uncertainty with a predefined uncertainty threshold; and when the uncertainty is greater than the predefined uncertainty threshold, blocking at least one data entry from the first set of input data. The term "uncertainty" refers to an epistemic situation involving imperfect or unknown information. Such uncertainty is applicable to predictions of future events, predetermined physical measurements, an unknown, and so forth. Optionally, the uncertainty is implemented as at least one of: a state uncertainty, an effect uncertainty, a response uncertainty. In operation, the uncertainty is caused by the at least one data entry of the first set of input data. The at least one data entry refers to a data entry of the first set of input data which causes a discrepancy and increases the uncertainty. Such data entry is often unreliable and thereby jeopardises the prediction of the at least one change. Optionally, the at least one data entry is utilised to train the at least one predictive domain model. The uncertainty is calculated by mapping if the at least one process action would implement the at least one change. When excessive disparity is observed between the two, it may be assumed that the at least one change is unreliable and thereby a corresponding process action is not implemented. Beneficially, blocking the at least one data entry from the first set of input data removes the uncertainty since the uncertainty was being caused by the at least one data entry. Moreover, calculating the uncertainty and thereon blocking the at least one data entry is beneficial since it safeguards the industrial method against unreliable data. Optionally, the method further comprises employing a monte carlo dropout algorithm to estimate the uncertainty. The monte carlo dropout algorithm refers to a class of computational algorithms that rely on repeated random sampling to obtain a distribution of a numerical quantity. Optionally, when the uncertainty is greater than the predefined uncertainty threshold, the method further comprises sending an alert to the at least one device associated with the entity. In such cases, the determination of the at least one change is halted, and the entity is to overlook the industrial process until the uncertainty is not reduced to a value equal to or lower than the predefined uncertainty threshold. In a second aspect, an embodiment of the present disclosure provides a system for managing an industrial process, the system comprising at least one data source and at least one processor, wherein the at least one data source and the at least one processor are communicably coupled, and wherein the at least one processor is configured to: obtain information pertaining to the industrial process; create at least one digital twin of the industrial process based at least on the information pertaining to the industrial process, wherein the at least one digital twin is dynamically updated in response to one or more changes to at least one asset, the asset comprised by the industrial process while the industrial process is executed; receive a first set of input data from the at least one data source, wherein the first set of input data is indicative of at least one parameter in the industrial process; assigning weights to the first set of input data, wherein the weights are indicative of contributions from one or more operations associated with the industrial process based on a pre-defined priority thereof to the industrial process; predict a first set of output data by executing at least one predictive domain model that is pre-trained, wherein the first set of output data is predicted based on the first set of input data , weighted combination of contributions from one or more operations associated with the industrial process, and the at least one digital twin; executing the at least one predictive domain model that is pretrained, using a mathematical addition or a mean of the first set of output data and the first set of input data, to generate a second set of input data, wherein the second set of input data is an accurate representation of the at least one predicted state of the industrial process; repeating the above steps for the at least one predictive domain model with updated values of the first set of input data until an acceptable value of a confidence factor is achieved; determine at least one change required in the industrial process, based on the at least one predicted state and at least one required state of the industrial process; and initiate, in response to the second set of input data, at least one process action to implement the at least one change, for optimising the industrial process. Throughout the present disclosure, the term "processor" refers to hardware, software, firmware, or a combination of these configured to control operation of the system. In this regard, the at least one processor performs several complex processing tasks. The at least one processor is communicably coupled to other components of the system device wirelessly and / or in a wired manner. In an example, the at least one processor may be implemented as a programmable digital signal processor (DSP). In another example, the at least one processor may be implemented via a cloud server that provides a cloud computing service. Optionally, a given data source is implemented as at least one of: a data source of the system, an external data source. Herein, when the given data source is implemented as the data source of the system, data pertaining to the system is stored at the data source of the system and when the given data source is implemented as the external data source, data pertaining to the system is stored at the external data source. Optionally, the input data comprises at least one of: historic data, realtime data, of the industrial process. Optionally, the at least one data source is implemented as a virtual sensor. Optionally, the at least one data source is implemented as at least one of: a physical sensor, a device. Optionally, the at least one predictive domain model comprises at least one of: a material model, a ball charge model, a liner wear model, a dynamic charge model, inferential trajectory model, ball breakage model, new ball ejection model, a material processing model, a data processing model, a waste management model, a packaging model, an assembly model, a printing model. Optionally, prior to the step of predicting the first set of output data, the at least one processor is configured to: create the at least one predictive domain model using at least one machine learning algorithm; and train the at least one predictive domain model using the first set of input data and the weighted combination of contributions from one or more operations associated with the industrial process. Optionally, the confidence factor is indicative of an accuracy of prediction and the acceptable value of the confidence factor is indicative of an efficient functioning of the industrial process. Optionally, when determining the at least one change required in the industrial process, the at least one processor is configured to: determine at least one proposed change based on the at least one predicted state and the at least one required state; simulate the at least one proposed change in the at least one digital twin of the industrial process to generate a simulated output state; assess whether the simulated output state is similar to the at least one required state; and when the simulated output state is similar to the at least one required state, implement the at least one proposed change as the at least one change required in the industrial process. Optionally, the at least one processor is further configured to: calculate an uncertainty of the at least one process action for implementing the at least one change; compare the uncertainty with a predefined uncertainty threshold; and when the uncertainty is greater than the predefined uncertainty threshold, block at least one data entry from the first set of input data. Optionally, the at least one processor is configured to employ adaptive data encryption and data obfuscation processing operations depending upon the at least one parameter of the industrial process. Optionally, the at least one processor is further configured to send a notification to at least one device associated with an entity, wherein the notification is indicative of the at least one change, and wherein the entity performs the at least one change to optimise the industrial process. Optionally, the at least one processor is further configured to identify adjustments that improve efficiency of one or more operations associated with the industrial process. Optionally, the weighted combination of contributions from one or more operations associated with the industrial process is computed using one or more weighting factors determined from at least one of: an analysis of a historical first set of input data for determining a set of values for the one or more weighting factors which enable the aggregate efficiency to be most representative of the one or more operations associated with the industrial process; and an application of operating perturbations to the one or more operations and utilising a corresponding detected change in the aggregate efficiency for iterating the set of values for the one or more weighting factors, for optimising the industrial process. Optionally, the analysis of a historical first set of input data employs at least one of: artificial intelligence, neural network analysis for determining the set of values for the one or more weighting factors. Optionally, the at least one data source is applied to one or more apparatus of an asset configured to perform the one or more operations associated with the industrial process. Optionally, the at least one processor is further configured to detect the one or more apparatus of the asset to which the at least one data source is applied, for determining whether the one or more apparatus of the asset is operating correctly. Optionally, the at least one processor is further configured to provide a condition-based maintenance plan for the one or more assets. Throughout the description and claims of this specification, the words "comprise" and "contain" and variations of the words, for example "comprising" and "comprises", mean "including but not limited to", and do not exclude other components, integers or steps. Moreover, the singular encompasses the plural unless the context otherwise requires: in particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise. Preferred features of each aspect of the invention may be as described in connection with any of the other aspects. Within the scope of this application, it is expressly intended that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and / or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. That is, all embodiments and / or features of any embodiment can be combined in any way and / or combination, unless such features are incompatible. DETAILED DESCRIPTION OF THE DRAWINGS Referring to Figure 1, illustrated is a process flow depicting steps of a method for managing an industrial process, in accordance with an embodiment of the present disclosure. At step 102, information pertaining to the industrial process is obtained. At step 104, at least one digital twin of the industrial process is created, based at least on the information pertaining to the industrial process, wherein the at least one digital twin is dynamically updated in response to one or more changes to at least one asset, the asset comprised by the industrial process while the industrial process is executed. At step 106, a first set of input data is received from at least one data source, wherein the first set of input data is indicative of at least one parameter in the industrial process, and wherein the at least one data source generates control signals for controlling the industrial process in real-time. At step 108, weights to the first set of input data are assigned, wherein the weights are indicative of contributions from one or more operations associated with the industrial process based on a pre-defined priority thereof to the industrial process. At step 110, a first set of output data is predicted by executing at least one predictive domain model that is pre-trained, wherein the first set of output data is predicted based on the first set of input data, weighted combination of contributions from one or more operations associated with the industrial process, and the at least one digital twin. At step 112, the at least one predictive domain model that is pre-trained is executed using a mathematical addition or a mean of the first set of output data and the first set of input data to generate a second set of input data, wherein the second set of input data is an accurate representation of the at least one predicted state of the industrial process. At step 114, the above steps for the at least one predictive domain model with updated values of the first set of input data are repeated until an acceptable value of a confidence factor is achieved. At step 116, at least one change required in the industrial process is determined, based on the at least one predicted state and at least one required state of the industrial process. At step 118, at least one process action to implement the at least one change is initiated in response to the second set of input data for optimising the industrial process. The aforementioned steps are only illustrative and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein. Referring to Figure 2, illustrated is a block diagram representing a system 200 for managing an industrial process, in accordance with an embodiment of the present disclosure. The system 200 comprises at least one data source (depicted as a data source 202) and at least one processor (depicted as a processor 204). The data source 202 and the processor 204 are communicably coupled. It may be understood by a person skilled in the art that the Figure 2 is merely an example for sake of clarity, which should not unduly limit the scope of the claims herein. The person skilled in the art will recognise many variations, alternatives, and modifications of embodiments of the present disclosure. Referring to Figure 3, illustrated is an exemplary process flow depicting steps of a method for managing an industrial process, in accordance with another embodiment of the present disclosure. At step 302, information pertaining to the industrial process is obtained. At step 304, at least one digital twin of the industrial process is created, based at least on the information pertaining to the industrial process, wherein the at least one digital twin is dynamically updated in response to one or more changes to at least one asset, the asset comprised by the industrial process while the industrial process is executed. At step 306, a first set of input data is received from at least one data source, wherein the first set of input data is indicative of at least one parameter in the industrial process, and wherein the at least one data source generates control signals for controlling the industrial process in real-time. Subsequently, weights to the first set of input data are assigned, wherein the weights are indicative of contributions from one or more operations associated with the industrial process based on a pre-defined priority thereof to the industrial process. At step 308, at least one predictive domain model is created using at least one machine learning algorithm. At step 310, the at least one predictive domain model is trained using the input data. At step 312, a first set of output data is predicted by executing the at least one predictive domain model that is pre-trained, wherein the first set of output data is predicted based on the first set of input data, weighted combination of contributions from one or more operations associated with the industrial process, and the at least one digital twin, and wherein the first set of output data is representative of at least one predicted state of the industrial process. At step 314, the at least one predictive domain model that is pre-trained is executed using a mathematical addition or a means of the first set of output data and the first set of input data, to generate a second set of input data, wherein the second set of input data is an accurate representation of the at least one predicted state of the industrial process. Subsequently, the above steps for the at least one predictive domain model with updated values of the first set of input data are repeated until an acceptable value of a confidence factor is achieved. At step 316, at least one change required in the industrial process is determined, based on the at least one predicted state and at least one required state of the industrial process. Herein, at step 316a, at least one proposed change is determined based on the at least one predicted state and the at least one required state. At step 316b, the at least one proposed change is simulated in the at least one digital twin of the industrial process to generate a simulated output state. At step 316c, whether the simulated output state is similar to the at least one required state is assessed. At step 316d, when the simulated output state is similar to the at least one required state, the at least one proposed change is implemented as the at least one change required in the industrial process. At step 318, at least one process action to implement the at least one change is initiated in response to the second set of input data for optimising the industrial process. At step 320, uncertainty of the at least one process action for implementing the at least one change is calculated. At step 322, the uncertainty is compared with a predefined uncertainty threshold. At step 324, when the uncertainty is greater than the predefined uncertainty threshold, at least one data entry is blocked from the first set of input data. At step 326, the at least one change is sent to at least one device associated with an entity, wherein the entity performs at least one change to optimise the industrial process. The aforementioned steps are only illustrative and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein.

Claims

d Claims1. A method for managing an industrial process, the method comprising:obtaining, by the at least one processor from a database, information pertaining to the industrial process;creating, by execution on the at least one processor, at least one digital twin of the industrial process based at least on the information pertaining to the industrial process,wherein the at least one digital twin is dynamically updated, by the at least one processor, in response to one or more changes to at least one asset that forms part of the industrial process, while the industrial process is executed;receiving, by the at least one processor communicably coupled to the at least one data source via a communication network, a first set of input data from the at least one data source, wherein the first set of input data is indicative of at least one parameter in the industrial process, and wherein the at least one data source generates control signals for controlling the industrial process in real-time;assigning, by execution on the at least one processor, weights to the first set of input data, wherein the weights are indicative of contributions from one or more operations associated with the industrial process based on a pre-defined priority thereof to the industrial process;predicting, by the at least one processor executing a pre-trained predictive domain model, a first set of output data, wherein the first set of output data is predicted based on the first set of input data, weighted combination of contributions from one or more operations associated with the industrial process, and the at least one digital twin;executing, by the at least one processor, the at least one predictive domain model that is pre-trained, using a mathematical addition or a mean of the first set of output data and the first set of input data, to generate a second set of input data, wherein the second set of input data is an accurate representation of at least one predicted state of the industrial process;repeating, by iterative numerical execution on the at least one processor, the above steps for the at least one predictive domain model with updated values of the first set of input data until an acceptable value of a confidence factor is achieved;determining, by the at least one processor, at least one change required in the industrial process, based on the at least one predicted state and at least one required state of the industrial process; andgenerating, by the at least one processor, one or more control signals in response to the second set of input data to initiate at least one physical action performed by equipment of the industrial process to cause the at least one change in the industrial process, thereby optimising the industrial process.

2. The method of claim 1, wherein input data comprises at least one of: historic data or real-time data of the industrial process, retrieved and processed by the at least one processor.

3. The method of any preceding claim, wherein the at least one data source is implemented as a virtual sensor, the virtual sensor being executed as a software-based sensing model by the at least one processor.

4. The method of claim 1 or 2, wherein the at least one data source is implemented as at least one of a physical sensor or a device, and wherein the at least one processor receives, via a communication link, data signals generated by the physical sensor or the device.

5. The method of any preceding claim, wherein the at least one predictive domain model comprises at least one of a material model, a ball charge model, a liner wear model, a dynamic charge model, an inferential trajectory model, a ball breakage model, a new ball ejection model, a material processing model, a data processing model, a waste management model, a packaging model, an assembly model, or a printing model, each of which is executed by the at least one processor.

6. The method of any preceding claim, wherein prior to the step of predicting the first set of output data, the method comprises creating the at least one predictive domain model using at least one machine learning algorithm executed by the at least one processor, and training the at least one predictive domain model using the first set of input data and theweighted combination of contributions from one or more operations associated with the industrial process by executing a training algorithm on the at least one processor.

7. The method of any preceding claim, wherein the confidence factor is indicative of an accuracy of prediction and the acceptable value of the confidence factor is indicative of an efficient functioning of the industrial process, the confidence factor being computed by the at least one processor.

8. The method of any preceding claim, wherein when determining the at least one change required in the industrial process, the method is configured to determine at least one proposed change based on the at least one predicted state and the at least one required state by the at least one processor, simulate the at least one proposed change in the at least one digital twin of the industrial process to generate a simulated output state by execution on the at least one processor, assess whether the simulated output state is similar to the required state by comparing simulated and required data via the at least one processor, and when the simulated output state is similar to the required state, implement the at least one proposed change as the at least one change required in the industrial process.

9. The method of any preceding claim, further comprising calculating an uncertainty of the at least one process action for implementing the at least one change by numerical computation executed by the at least one processor, comparing the uncertainty with a predefined uncertainty threshold via the at least one processor, and when the uncertainty is greater than the predefined uncertainty threshold, blocking at least one data entry from the first set of input data by the at least one processor.

10. The method of any preceding claim, configured to employ adaptive data encryption and data obfuscation processing operations depending upon at least one parameter of the industrial process, the encryption or obfuscation operations being executed by the at least one processor.

11. The method of any preceding claim, further comprising sending a notification to at least one device associated with an entity, wherein the notification is indicative of the at leastone change, and wherein the entity performs the at least one change to optimise the industrial process, the notification being transmitted by the at least one processor via a communication network.

12. The method of any preceding claim, wherein the weighted combination of contributions from one or more operations associated with the industrial process is computed using one or more weighting factors determined from at least one of an analysis of a historical first set of input data for determining a set of values for the one or more weighting factors or an application of operating perturbations to the one or more operations and utilising a corresponding detected change in aggregate efficiency for iterating the set of values of the weighting factors, wherein the analysis and numerical computations are executed by the at least one processor.

13. The method of claim 12, wherein the analysis of a historical first set of input data employs at least one of artificial intelligence or neural network analysis executed by the at least one processor.

14. The method of claim 1, wherein the at least one physical action is performed by an actuator operated in response to the one or more control signals.

15. The method of claim 1, wherein the one or more control signals are provided to a control interface associated with the industrial process to initiate the at least one physical action.16 The method of claim 15, wherein the control interface is configured to operate an actuator based on the one or more control signals.

17. A system for managing an industrial process, the system comprising at least one data source and at least one processor, wherein the at least one data source and the at least one processor are communicably coupled via a communication network, and wherein the at least one processor is configured to:obtain, by execution on the processor, information pertaining to the industrial process from a database;create, by execution on the processor, at least one digital twin of the industrial process based at least on the information pertaining to the industrial process, wherein the at least one digital twin is dynamically updated in response to one or more changes to at least one asset comprised by the industrial process while the industrial process is executed;receive, via the communication network, a first set of input data from the at least one data source, the first set of input data being indicative of at least one parameter in the industrial process;assign, by execution on the processor, weights to the first set of input data, the weights being indicative of contributions from one or more operations associated with the industrial process based on a pre-defined priority thereof;predict, by executing on the processor, a first set of output data using at least one pretrained predictive domain model, the first set of output data being predicted based on the first set of input data, a weighted combination of contributions from the one or more operations, and the at least one digital twin;execute, by the processor, the pre-trained predictive domain model using a mathematical addition or a mean of the first set of output data and the first set of input data to generate a second set of input data representative of at least one predicted state of the industrial process;repeat, by the processor, execution of the pre-trained predictive domain model with updated values of the first set of input data until an acceptable value of a confidence factor is achieved;determine, by the processor, at least one change required in the industrial process based on the at least one predicted state and at least one required state of the industrial process; andgenerate, by the processor, one or more control signals in response to the second set of input data to initiate at least one physical action performed by equipment of the industrial process to cause the at least one change, thereby optimising the industrial process.

18. The system of claim 17, wherein input data comprises at least one of historic data or real-time data of the industrial process, retrieved and processed by the at least one processor.

19. The system of claim 17 or 18, wherein the at least one data source is implemented as at least one of a physical sensor or a device, and wherein the at least one processor receives, via a communication link, data signals generated by the physical sensor or the device.

20. The system of any of claims 17 to 19, wherein the at least one predictive domain model comprises at least one of a material model, a ball charge model, a liner wear model, a dynamic charge model, an inferential trajectory model, a ball breakage model, a new ball ejection model, a material processing model, a data processing model, a waste management model, a packaging model, an assembly model, or a printing model, each being executable by the at least one processor.

21. The system of any of claims 17 to 20, wherein prior to predicting the first set of output data, the at least one processor is configured to create the at least one predictive domain model using at least one machine learning algorithm and train the at least one predictive domain model using the first set of input data and the weighted combination of contributions from one or more operations associated with the industrial process.

22. The system of any of claims 17 to 21, wherein the confidence factor is indicative of an accuracy of prediction and the acceptable value of the confidence factor is indicative of an efficient functioning of the industrial process, the confidence factor being computed by the at least one processor.

23. The system of any of claims 17 to 22, wherein the at least one processor is further configured to determine at least one proposed change, simulate the proposed change in the digital twin, assess whether a simulated output state is similar to a required state, and, when similar, implement the proposed change as the at least one change required in the industrial process.

24. The system of any of claims 17 to 23, wherein the at least one processor is further configured to calculate uncertainty, compare the uncertainty with a predefined threshold, and block data entries based on the result of the comparison.

25. The system of any of claims 17 to 24, wherein the weighted combination of contributions from one or more operations associated with the industrial process is computed using one or more weighting factors determined from at least one of an analysis of a historical first set of input data or an application of operating perturbations, wherein the analysis and numerical computations are executed by the at least one processor.

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