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The purpose of this study was to develop a suitable module for digital conservation of traditional knowledge of medicinal plants (MPs) used by tribal communities living in the Anuppur district of Madhya Pradesh, Central India.

The research used a qualitative approach to gather the data of MPs through the use of literature review and field survey. Based on the acquired data, a prototype digital learning system was constructed and assessed. This study used digital learning technologies to assess the requirements for transmitting traditional knowledge of important MPs used by tribal communities so that people can absorb and conserve them.

Over time, the focus on the digital conservation of traditional MP’s knowledge has progressively increased globally. Despite the rise in this field of study, information technology methods to preserve and distribute traditional knowledge of MPs have remained a few. When adopting digital learning to maintain traditional knowledge of MPs, it was discovered that it would be necessary to engage with relevant knowledge keepers, use multimedia, and provide content in local languages.

This study helps in conservation of important MP species that are having biologically important therapeutic compounds meant for treating various ailments. Older generations of various tribal communities mainly hold traditional knowledge of important MPs, and unless it is preserved, it will perish along with its caretakers.

It is worth looking at a digital platform that can help future generations to maintain traditional knowledge of MPs, as it is a dynamic and ever-changing, it must involve a digital tool for its future conservation. Current methods for maintaining traditional knowledge of MPs were ineffective and constrained by space and time.

The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources.

Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms.

Research indicates that integral control parameters (K_i) affect outputs substantially more than proportional control parameters (K_p). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676.

This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.