The present disclosure claims priority from the provisional Indian patent application Reference Number: E-3/20953/2017-DEL/ Application Number: 201711022551 filed on June 28,b2017, and all the contents of the provisional application are included herein.
FIELD OF THE INVENTION
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The title relates to the field of intelligent container design for holding 1 fluids. More specifically the title relates to the design of a pressure vessel for holding fluids with significant differential pressure with respect to the ambient.
BACKGROUND OF THE INVENTION
In the Prior Art, various applications of building pressure vessels have been described. Some exemplary references are given as follows:
When the pressure vessel holds gases or liquids at a pressure substantially different from the ambient pressure, the pressure differential could be dangerous, and fatal accidents have occurred in the history of pressure vessel development and operation. Consequently, pressure vessel design, manufacture, and operation are regulated by engineering authorities backed by legislation. For these reasons, the definition of a pressure vessel varies from country to country, but involves parameters such as maximum safe operating pressure and temperature, and are engineered with a safety factor, corrosion allowance, minimum design

temperature (for brittle fracture), and involve nondestructive testing, such as ultrasonic testing, radiography, and pressure tests. In the United States, as with many other countries, it is the law that vessels over a certain size and pressure (15 PSI) be built to a Code. In the United States that Code is the Boiler and Pressure Vessel Code (BPVC) of the American Society of Mechanical Engineers (ASME). These vessels also require an Authorized Inspector to sign off on every new vessel constructed.
In the Prior Art, various references discuss the design of Pressure Vessels using the ASME codes. Very many books and scholarly papers have been written on this topic and is a well-studied subject. "Design of Pressure Vessels using ASME Code, Section VIII, Div.l" is a paper in IJAERS/Volume I/Issue II/Jan-March 2012, pp 228-234, that describes one such process. Companies such as "Codeware, Inc." have products which could run on Desktops, such as "COMPRESS", which is yet another example of desktop based pressure vessel design. There are many instances which describe that various inputs including the codes are input to a processing block on a desktop and the calculations are made to evolve output.
Recently 'Cloud Computing", also referred to as 'Cloud' has been used for the pressure vessel design. Cloud computing is a type of Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing, resources (e.g., computer networks, servers, storage, applications and 'services), which can be rapidly provisioned and released with minimal management effort. Pressure vessel design can also be done on Cloud and has been a recent trend as depicted by Cloud version of "COMPRESS"

-from-Godewarevlhcr Yet another example is "HPC-Cloud-based design of high pressure vessels" by Fortissimo (www.i4ms.edu).
Recent advances in 3D printing have also been evaluated for prior art. WO2015142862A1 describes "Pressure vessels, design and method of manufacturing using additive printing" and has a Method and design of a pressure vessel having an internal supportive structure that reduces pressure forces applied to the external shell of the vessel by distributing such forces via internal bonds mostly connected to a central supporting element. The method and design allow making much lighter and stronger pressure vessels and containers using additive manufacturing technology, known as 3D printing.
Based on the above prevalent practices, their present limitations are given below:
Firstly, when a pressure vessel design is made using templates, input parameters and design/construction codes, the options for selection of material or procurement of the specified dimensions may not be trivial or even possible.
Secondly, the logistics of movement of material may add substantial costs to the actual building of the pressure vessel, even though the design itself may be optimal.
In view of the above prior art, there is a need to evolve a more intelligent and comprehensive mechanism of cloud based, more optimal design of pressure vessels. In prior art, there is no mention of using forward chaining of logistics in the design mechanism of the pressure vessels.
SUMMARY OF THE INVENTION
The present disclosure describes systems and a method for evolving an intelligent pressure vessel design.

In an exemplary mode for the disclosure, the system for evolving an intelligent pressure vessel design includes an input parameter system that includes a plurality of input parameters and also includes a design and construction codes system storing a plurality of design and construction codes. As per an aspect of the disclosure is described that the system for evolving an intelligent pressure vessel design further includes a pre¬processing system storing a plurality of design templates and corresponding input parameters and design and constructions codes data, and further storing a plurality of design rules; and also a sourcing data system storing a plurality of sourcing data.
Yet another aspect of the disclosure describes a processing block for iteratively processing a plurality of optimal pressure vessel designs with corresponding optimization scores, using the input parameters system, the design and constructions code system, the pre-processing system, and the sourcing data system. A further aspect of the disclosure elaborates an output that uses the plurality of optimal pressure vessel designs with corresponding optimization scores from the processing block, to evolve a ranked list of optimal pressure vessel designs.
As per yet another aspect of the disclosure, the input parameters
system includes maximum pressure, wind data, shape of the pressure vessel,
weather data, and seismic data; and the design and construction codes
system includes BPVC of the ASME of North America, Pressure
Equipment Directive of the EU (PED), Japanese Industrial
Standard (US), CSA B51 in Canada, and Australian Standards in Australia. Another aspect of the disclosure describes that the sourcing data system includes availability of various materials, their costs, their sourcing distance and hence its impact on shipping.

In-yet anotheraspect of the disclosure, a method and a system for evolving an intelligent pressure vessel design is described, where the system comprises at least a processor and a memory, wherein the memory and the processor are functionally coupled to each other. The system further includes an input parameter system that includes a plurality of input parameters and also includes a design and construction codes system storing a plurality of design and construction codes. As per an aspect of the disclosure the system further includes a pre-processing system storing a plurality of design templates and corresponding input parameters and design and constructions codes data, and further storing a plurality of design rules; and also a sourcing data system storing a plurality of sourcing data.
Yet another aspect of the disclosure describes a processing block for iteratively processing a plurality of optimal pressure vessel designs with corresponding optimization scores, using the input parameters system, the design and constructions code system, the pre-processing system, the sourcing data system and is functionally coupled to the processor. A further aspect of the disclosure elaborates an output that uses the plurality of optimal pressure vessel designs with corresponding optimization scores from the processing block, to evolve a ranked list of optimal pressure vessel designs.
BRIEF DESCRIPTION OF THE DRAWINGS
For a more complete understanding of the present invention and the advantages thereof, reference is now made to the following descriptions taken in connection with the accompanying drawings in which:
Figure 1 describes a system (100) configured for evolving an intelligent pressure vessel design;

Figure 2 depicts a flow chart (200) for a method corresponding to the system (100), to evolve an intelligent pressure vessel design, in which one or more steps of the logic flow can be mapped to various system blocks of system (100) of Figure 1; and
Figure 3 depicts a system (300) with a memory and a processor configured to evolve an intelligent pressure vessel design, wherein the memory and the processor are functionally coupled to each other.
DETAILED DESCRIPTION
The present disclosure describes a system and method for evolving an intelligent pressure vessel design.
The system could also be a computer readable medium, functionally coupled to a memory, where the computer readable medium is configured to implement the exemplary steps of the method. The system can be implemented as a stand-alone solution, as a Software-as-a-Service (SaaS) model or a cloud solution or any combination thereof.
Thus, the systems (100) and (300) and the method (200) in accordance with the present disclosure are deployable across a plurality of platforms using heterogeneous server and storage farms spread across geographies for better availability and high response time.
Figure 1 describes a system (100) configured for evolving an intelligent pressure vessel design. The system (100) includes an input parameter system (102) including a plurality of input parameters. The input parameters, in an exemplary manner include maximum pressure, wind data, shape of the pressure vessel, weather data, and seismic data. The system

(100) further includes a design and construction codes system (104) storing a plurality of design and construction codes, which in exemplary manner may include BPVC of the ASME of North America, Pressure Equipment Directive of the EU (PED), Japanese Industrial Standard (JIS), CSA B51 in Canada, and Australian Standards in Australia.
The system (100) further includes a pre-processing system (106) storing a plurality of design templates and corresponding input parameters and design and constructions codes data, and further storing a plurality of design rules. The system (100) also further includes a sourcing data system. (108) storing a plurality of sourcing data. Examples of sourcing data are availability of various materials, their costs, their sourcing distance and hence its impact on shipping.
The system (100) also includes a processing block (110) for iteratively processing a plurality of optimal pressure vessel designs with corresponding optimization scores, wherein the processing block (110) uses the input parameters system (102), the design and constructions code system (104), the pre-processing system (106), and the sourcing data system (108). The processing may make use of methods selected from statistical methods, numerical methods, expert systems based methods, artificial intelligence based methods, machine learning methods and any combination thereof.
The system (100) further includes an output (112) that uses the plurality of optimal pressure vessel designs with corresponding optimization scores from the processing block (110), to evolve a ranked list of optimal pressure vessel designs.
Any one or any combination of the input parameters system (102), the design and construction codes system (104), the pre-processing system

(106), the sourcing data system (108), the processing block (110) and the output (112), are on any combination of a desktop and cloud.
We now refer to Figure 2 which describes a flowchart for various steps of a method (200) for evolving an intelligent pressure vessel design. This method (200) is consistent with the system (100) described in Figure 1, and is explained in conjunction with components of the system (100). Step (202) describes receiving a plurality of input parameters corresponding to the pressure vessel design. The input parameters, in an exemplary manner include maximum pressure, wind data, shape of the pressure vessel, weather data, and seismic data. This is corresponding to the input parameters system (102) of Figure 1.
Step (204) describes the step of receiving a plurality of design and construction codes corresponding to the pressure vessel design, wherein the plurality of design and construction codes reside in the design and construction codes system (104) of Figure 1. The design and construction codes, which in exemplary manner may include BPVC of the ASME of North America, Pressure Equipment Directive of the EU (PED), Japanese Industrial Standard (JIS), CSA B51 in Canada, and Australian Standards in Australia.
Step (206) describes accessing a plurality of design templates and corresponding input parameters and design and constructions codes data, and further accessing a plurality of design rules. These design templates and design rules are stored in the pre-processing system (106) of Figure 1. Step (208) depicts accessing a plurality of sourcing data from the sourcing data system (108) of Figure 1. Examples of sourcing data are availability of various materials, their costs, their sourcing distance and hence its impact on shipping.

-Step (210) describes" iteratively processing a plurality of optimal pressure vessel designs with corresponding optimization scores, wherein the processing uses the input parameters, the plurality of design and construction codes, the plurality of design templates and corresponding input parameters and design and constructions codes data, the plurality of design rules and the sourcing data. The processing step (210) is implemented in the Processing Block (110) of Figure 1.
The optimization scores are, in an exemplary manner, computed using cost as a parameter. Cost may include, the material cost. As per step (202) the input parameters such as expected maximum differential pressure, the seismic data at the location of erection, the maximum wind speed at different heights etc. are obtained. The materials which are acceptable are say copper or stainless steel. This is based on the received plurality of design and construction codes as per step (204). The total amount of material required will be calculated on the basis of the height and diameter of the pressure vessel, if cylindrical, or otherwise if it is other than cylindrical. The shape of the pressure vessel will be calculated using the input parameters obtained in step (202) as well as the plurality of design templates and design rules obtained from step (206). The material amount will also depend on the thickness of the wall of the vessel to withstand the differential pressure, the wind force and the potential seismic impact. The cost will also, in an exemplary manner, include the fabrication cost and erection cost. These types of costs are already normally included in the calculations.
One aspect of the disclosure adds a few more variables to the cost function, in particular the plurality of sourcing parameters obtained from step (208). In an exemplary manner, the plurality of sourcing data includes, cost of transportation and it may turn out that if we were to use stainless steel as the material and fabrication cost is lower, however, if we include

the transportation cost for it, the overall cost may be much more than that of the pressure vessel being built using copper. Thus based on the computation in the step (210), corresponding to the processing block (110) of Figure 1, there will be a plurality of optimal pressure vessel designs with corresponding optimization scores.
Step (212) describes evolving an output of a ranked list of optimal pressure vessel designs, using the plurality of optimal pressure vessel designs with corresponding optimization scores. This is corresponding to the output (112) of Figure 1.
Now we refer to Figure 3 which describes a system (300) for evolving an intelligent pressure vessel design, the system (300) comprising at least a processor and a memory (301), wherein the memory (301) and the processor are functionally coupled to each other, the system (300) further includes an input parameter system (102) including a plurality of input parameters. The input parameters, in an exemplary manner include maximum pressure, wind data, shape of the pressure vessel, weather data, and seismic data. The system (300) further includes a design and construction codes system (104) storing a plurality of design and construction codes, which in exemplary manner may include BPVC of the ASME of North America, Pressure Equipment Directive of the EU (PED), Japanese Industrial Standard (JIS), CSA B51 in Canada, and Australian Standards in Australia.
The system (300) further includes a pre-processing system (106) storing a plurality of design templates and corresponding input parameters and design and constructions codes data, and further storing a plurality of design rules. The system (300) also further includes a sourcing data system (108) storing a plurality of sourcing data. Examples of sourcing data are

-availability-of various materials, their costs, their sourcing distance and hence its impact on shipping.
The system (300) also includes a processing block (110) for iteratively processing a plurality of optimal pressure vessel designs with corresponding optimization scores, wherein the processing block (110) uses the input parameters system (102), the design and constructions code system (104), the pre-processing system (106), the sourcing data system (108) and is also coupled to the processor to compute optimization scores. The processing may make use of methods selected from statistical methods, numerical methods, expert systems based methods, artificial intelligence based methods, machine learning methods and any combination thereof.
The system (300) further includes an output (112) that uses the plurality of optimal pressure vessel designs with corresponding optimization scores from the processing block (110), to evolve a ranked list of optimal pressure vessel designs.
The systems (100) and (300) and the method (200) are deployable using multiple hardware and integration options, such as, for example, cloud infrastructure, standalone solutions mounted on mobile hardware devices, third-party platforms and system solutions etc. and is advantageously facilitated to be validated using biometric and electronic verifications like e-KYC (Know Your Customer).
There are several advantages of the system and method of intelligent design of pressure vessels proposed in the disclosure. One advantage is that the system and method include pre-processing wherein various templates could be evaluated for similarity and hence the response time for a new design is lessened. The use of historical template data reduces computation and draws upon optimal designs already created for similar purposes. Yet

another-advantageisthat^the processing can be done either on a desktop or any distributed computing configuration eg. cloud. One more advantage is that of more realistic design due to inclusion of sourcing data. Sourcing data also may enable global optimization since the sourcing data can also be made of the cost function used in processing block. Yet another advantage is that the possible iterative interactions 1. between pre-processing and processing block , and 2. the processing block and sourcing data, enable automatic redesigning without manual intervention even if a single change is made. In an exemplary mode, this may also result into single dish to double dish type pressure vessel design with least interventions.

WE CLAIM:
1. A system (100) for evolving an intelligent pressure vessel design, the system (100) comprising:
• an input parameter system (102) including a plurality of input parameters;
• a design and construction codes system (104) storing a plurality of design and construction codes;
• a pre-processing system (106) storing a plurality of design templates and corresponding input parameters and design and constructions codes data, and further storing a plurality of design rules;
• a sourcing data system (108) storing a plurality of sourcing data;
• a processing block (110) for iteratively processes a plurality of optimal pressure vessel designs with corresponding optimization scores, wherein the processing block (110) uses the input parameters system (102), the design and constructions code system (104), the pre-processing system (106), and the sourcing data system (108); and
• an output (112) that uses the plurality of optimal pressure vessel designs with corresponding optimization scores from the processing block (110), to evolve a ranked list of optimal pressure vessel designs.
2. The system (100) as claimed in claim 1, wherein
• the input parameters system (102) comprises maximum pressure, wind data, shape of the pressure vessel, weather data, and seismic data; and

• the design and construction codes system (104) comprises BPVC
of the ASME of North America, Pressure Equipment
Directive of the EU (PED), Japanese Industrial
Standard (JIS), CSA B51 in Canada, and Australian Standards in
Australia.
3. The system (100) as claimed in claim 1, wherein:
• the sourcing data system (108) comprises availability of various
materials, their costs, their sourcing distance and hence its impact
on shipping.
4. The system (100) as claimed in claim 1, wherein
• wherein the processing block (110) iteratively processes a
plurality of optimal pressure vessel designs with corresponding
optimization scores, uses methods selected from statistical
methods, numerical methods, expert systems based methods,
artificial intelligence based methods, machine learning methods
and any combination thereof.
5. The system (100) as claimed in claim 1, wherein
• any one or any combination of the input parameters system (102),
the design and construction codes system (104), the pre¬
processing system (106), the sourcing data system (108), the
processing block (110) and the output (112), are on any
combination of a desktop and cloud.
6. A method (200) for evolving an intelligent pressure vessel design, the
method (200) comprising the steps of:

• receiving a plurality of input parameters corresponding to the pressure vessel design;
• receiving a plurality of design and construction codes corresponding to the pressure vessel design;
• accessing a plurality of design templates and corresponding input parameters and design and constructions codes data, and further accessing a plurality of design rules;
• accessing a plurality of sourcing data;
• iteratively processing a plurality of optimal pressure vessel designs with corresponding optimization scores, wherein the processing uses the input parameters, the plurality of design and constructions codes, the plurality of design templates and corresponding input parameters and design and constructions codes data, the plurality of design rules and the sourcing data; and
• evolving an output of a ranked list of optimal pressure vessel designs, using the plurality of optimal pressure vessel designs with corresponding optimization scores
7. The method (200) as claimed in claim 6, wherein:
• the input parameters comprise maximum pressure, wind data, shape of the pressure vessel, weather data, and seismic data;
• the design and construction codes comprise BPVC of the ASME of North America, Pressure Equipment Directive of the EU (PED), Japanese Industrial Standard (JIS), CSA B51 in Canada, and Australian Standards in Australia; and
• the sourcing data comprises availability of various materials,

their costs, 'their sourcing distance and hence its impact on shipping.
8. The method (200) as claimed in claim 6, wherein:
• the iterative processing of the plurality of optimal pressure vessel designs with corresponding optimization scores, uses methods selected from statistical methods, numerical methods, expert systems based methods, artificial intelligence based methods, machine learning methods and any combination thereof.
9. A system (300) for evolving an intelligent pressure vessel design, the
system (30.0) comprising at least a processor and a memory (301), wherein
the memory (301) and the processor are functionally coupled to each other,
the system (300) further comprising:
• an input parameter system (102) including a plurality of input parameters;
• a design and construction codes system (104) storing a plurality of design and construction codes;
• a pre-processing system (106) storing a plurality of design templates and corresponding input parameters and design and constructions codes data, and further storing a plurality of design rules;
• a sourcing data system (108) storing a plurality of sourcing data;
• a processing block (110) for iteratively processes a plurality of optimal pressure vessel designs with corresponding optimization scores, wherein the processing block (110) uses the input parameters system (102),

-the-design-and"co"n^ (104), the pre-processing system
(106), and the sourcing data system (108) and also coupled to the processor to compute scores; and
• an output (112) that uses the plurality of optimal pressure vessel designs with corresponding optimization scores from the processing block (110), to evolve a ranked list of optimal pressure vessel designs.
10. The system (300) as claimed in claim 9, wherein:
• the input parameters system (102) comprises maximum pressure, wind data, shape of the pressure vessel, weather data, and seismic data;
• the design and construction codes system (104) comprises BPVC of the ASME of North America, Pressure Equipment Directive of the EU (PED), Japanese Industrial Standard (JIS), CSA B51 in Canada, and Australian Standards in Australia; and
• the sourcing data system (108) comprises availability of various materials, their costs, their sourcing distance and hence its impact on shipping.