Expert Systems are interactive and reliable computer-based decision-making systems that use both facts and heuristics for solving complex decision-making problems. Generally, the cyclic voltammetry (CV) experiments are executed a random number of times (cycles) to get a stable production of power. However, presently there are not many algorithms or models for predicting the power generation stable criteria in microbial fuel cells. For stability analysis of Medicinal herbs’ CV profiles, an expert system driven by the augmented K-means clustering algorithm is proposed. Our approach requires a dataset that contains voltage-current relationships from CV experiments on the related subjects (plants/herbs). This new approach uses feature engineering and augmented K-means clustering techniques to determine the cycle number beyond which the CV curve stabilizes. We obtain an excellent estimate of the required CV cycles for getting a stable Voltage versus Current curve in this approach. Moreover, this expert system would reduce the time needed and the money spent on running additional and superfluous CV experiments cycles. Thus, it would streamline the process of Bacterial Fuel Cells production using the CV of medicinal herbs.

As technologists worldwide focus on zero-emissions energy for power generation, fuel cell (FC) technology paves the way for it. Like a battery, the fuel cells undergo an electrochemical reaction to generate electricity. Unlike battery cells, fuel cells require a continuous supply of fuel and oxygen to sustain the chemical reaction. A significant difference between fuel cell and other electrochemical power generation devices is that FC can be used only after system integration. The fuel cell unit consists of a stack of individual cells. Each cell in the stack has one positive electrode (cathode), one negative electrode (anode), a solid or liquid electrolyte for permitting appropriate ions from one electrode to another and a catalyst for accelerating the reactions at the electrodes. The fuel cells are classified based on various parameters like temperature range, type of electrolyte used, the fuel’s physical state, etc. As fuel cells possess different operational variables, it is difficult to classify them. The fuel cells are classified as hydrogen-oxygen, fossil, alkaline, alcohol and hydrocarbon fuel cells based on the fuel type. Primary and Secondary fuel cells are classified based on the reactants passed and their reaction in the fuel cell. In the former, the reactants are added only once. The by-products are discarded whereas, in the latter, reactants are passed several times as they are regenerated using various methods. The fuel cells like polymer Electrolyte Membrane, solid oxide, direct methanol, proton exchange membrane, molten carbonate, phosphoric acid and microbial fuel cells are commonly used in the market. The fuel cell technologies right from Humphry Davy’s fuel cells to hydrogen fuel cells focus mainly on lower environmental impact with high efficiency. Thus, these replace the fossil fuel systems where fuel is generated from the decomposition of buried dead organisms and eliminate pollution.

Fuel cells are more effective than gas engines. They are ideal for toys, vehicles, commercial buildings like medical health centres, educational institutions, financial corporations, residential facilities, military applications (battlefield power) and large industrial corporations. Various fuel cells have an extensive assortment of applications, ranging from robust backup power systems to power consumption for mobile transports like electric buses, trains, and heavy chemical transport trucks.

Moreover, the productive use of hydrogen fuel cell will reduce the greenhouse gases emitted. As fuel cells do not require any gas or oil, non-oil-producing nations will benefit, reducing their expenditure. Microbial Fuel cells (MFC) use bacteria as the catalyst, thus oxidizing organic and inorganic matter from the synergistic interactions of immobilized cell populations, significantly attenuating electron transfer resistance biofilm and solid electrode for adequate power generation. The taxonomy of the microbial fuel cell is depicted in

The feature engineering (FE) technique is used to obtain the exact features required for processing the data from our dataset and predict the results. It assists in creating a new feature set from the existing features. As FE helps isolate and highlight the critical information to make the algorithm focus on the significant key aspect, data scientist often prefers FE as the best model for improving the performance. The clustering technique is used to group the data based on the similarity. Thereby, more similar data lies in one group and dissimilar data in another. Because of this capability, data reduction can be attained, thus making the prediction better. K-means, an unsupervised learning algorithm, is used for clustering. We intend to choose this for our non-categorical data as it works better.

Conventional fuel cells have many system-specific deficiencies like methanol cross-over, CO_{2} evolution, and anode kinetics to be considered before preferring other power generation techniques like internal combustion engines [_{2} from fuel and oxidants by the use of potassium hydroxide. Because of their biodegradable fuel and mild operating conditions, microbial fuel cells have been observed appropriate fit for low-power applications with environmental friendliness like bioelectricity, biosensors, wastewater treatment and bio-hydrogen production [

Moreover, in this work, augmented K-means clustering-based modelling is used for systems-driven stability analysis of cyclic voltammetry (CV) profiles of medicinal herb extracts (

Cyclic voltammetry was adopted to explore medicinal herbs’ electrochemical characteristics (

The electrochemically energetic breed concentration produced and eradicated in the reactions is directly proportionate to the reductive and oxidative potential peak current. Henceforth, it becomes essential to apply incessantly; several electrical potential cycles over time ensure the stable CV profiles’ realization using the adequate original information. Besides being a very trying, resource and time-consuming activity, it turns out to be significant for the CV analysis to obtain the exact measures predicting the accurate value of the electric current

Implementing serial cyclic voltammetric (CV) analysis upon test sample (e.g., herbal extract) could electrochemically simulate applications of repeated electron-donating and withdrawing processes to reflect sequential responses of electrochemical reduction and oxidation. As

The graphs portrayed in

As the dataset taken does not provide meaningful categories, feature engineering offers the solution to produce significant deductions. The following six new features were generated to test on the available medicinal herb dataset.

The cumulative voltage variance

The cumulative current variance

Product of cumulative voltage variance and cumulative current variance

The cumulative variance of the cumulative voltage variance

The cumulative variance of the cumulative current variance

The cumulative variance of the product of cumulative voltage variance and cumulative current variance.

The significance of these features is that they can give the approximate trend of changes in voltage-current pairs. This was quite evident from the values of the new generated features.

A glimpse of the new feature set for the four medicinal herbs:

0: Voltage

1: Current

2: Cycle Number

3: Cumulative Voltage Variance

4: Cumulative Current Variance

5: Product of Cumulative Voltage Variance and Cumulative Current Variance

6: Cumulative Variance of Cumulative Voltage Variance

7: Cumulative Variance of Cumulative Current Variance

8: Cumulative Variance of Product of Cumulative Voltage Variance and Cumulative Current Variance.

Multiple features and their permutations were tried, and since, there is a solid relationship between every feature as they were handcrafted. It becomes relatively easy to find the decision boundary on observing the trends, and in fact, our algorithms were also supportive of these trends. Moreover, before any clustering, feature scaling was applied. The augmented K-means Clustering approach applied here makes use of the L2 norm. Euclidean distance is the shortest distance between two points in an N-dimensional space, also known as Euclidean space. It is used as a standard metric to measure the similarity between two data points. It is also referred to as the Euclidean norm, Euclidean metric, L2 norm, L2 metric and Pythagorean metric.

K-means algorithm is an iterative algorithm used for partitioning the non-categorical data into K clusters (a group of similar data items). It iterates until all data items are grouped into distinct non-overlapping clusters (with duplication). K-means clustering is very popular in cluster analysis where it partitions n—input data points into K clusters fixed apriori. The data points are assembled into a cluster based on the nearest mean value, i.e., the data points in the cluster will have the closest mean value. This is how the data is partitioned in Voronoi cells. Each cluster will have one cluster head. When all the data points are placed into K clusters, the cluster heads’ positions are recalculated. The classical K-means algorithm can be sensitive to the initial cluster centre. Moreover, distinct initial cluster centres might have a significant influence on the final clustering outcomes. Due to this reason, this algorithm is considered to be highly volatile. Therefore, to overcome this limitation, we introduce the augmented K-Means Clustering Algorithm, as shown in

The augmented K-Means Clustering machine learning approach is deployed in this research for generating the model phase-plane sketches of current output versus applied voltage via the CV profile scan cycles. Consequently, to devise appropriate power generation and storage systems, we need to investigate this unstable condition. Hence, this work establishes the augmented K-Means Clustering machine learning approach to predict the microbial fuel cell’s time duration to generate a stable power output. Further, we can measure the output voltage as a time-period function (around one thousand six hundred hours). The pictorial representation of all four medicinal herbs (

Moreover, from the slope present in

In this work, K’s optimal value is determined using the Gap Statistic algorithm [

The plot for Cycle Number

Clustering Algorithm | K-Value Selection Algorithm | K-value | Stable time-period(cycle number) |
---|---|---|---|

Classical K-means | Elbow method | 6 | 81 |

Classical K-means | Average Silhouette | 4 | 75 |

Classical K-means | Gap Statistic | 2 | 63 |

Augmented K-means (Proposed) | Elbow method | 6 | 57 |

Augmented K-means (Proposed) | Average Silhouette | 4 | 56 |

Augmented K-means (Proposed) | Gap Statistic | 2 | 51 |

Clustering Algorithm | K-Value Selection Algorithm | K-value | Stable time-period(cycle number) |
---|---|---|---|

Classical K-means | Elbow method | 6 | 84 |

Classical K-means | Average Silhouette | 4 | 78 |

Classical K-means | Gap Statistic | 2 | 66 |

Augmented K-means (Proposed) | Elbow method | 6 | 63 |

Augmented K-means (Proposed) | Average Silhouette | 4 | 61 |

Augmented K-means (Proposed) | Gap Statistic | 2 | 55 |

Clustering Algorithm | K-Value Selection Algorithm | K-value | Stable time-period(cycle number) |
---|---|---|---|

Classical K-means | Elbow method | 6 | 75 |

Classical K-means | Average Silhouette | 4 | 70 |

Classical K-means | Gap Statistic | 2 | 63 |

Augmented K-means (Proposed) | Elbow method | 6 | 57 |

Augmented K-means (Proposed) | Average Silhouette | 4 | 54 |

Augmented K-means (Proposed) | Gap Statistic | 2 | 48 |

Clustering Algorithm | K-Value Selection Algorithm | K-value | Stable time-period(cycle number) |
---|---|---|---|

Classical K-means | Elbow method | 6 | 78 |

Classical K-means | Average Silhouette | 4 | 72 |

Classical K-means | Gap Statistic | 2 | 61 |

Augmented K-means (Proposed) | Elbow method | 6 | 52 |

Augmented K-means (Proposed) | Average Silhouette | 4 | 51 |

Augmented K-means (Proposed) | Gap Statistic | 2 | 45 |

The generation of stable power in microbial fuel cells seems to be an arduous task, and for this reason, the practical applications such cell becomes limited. Moreover, in this research, we highlight the problem in modelling CV profiles of the medicinal herbs (

The authors sincerely appreciate the data support from the Microbial Fuel Cells (MFCs)^{SDG} project conducted in Biochemical Engineering Laboratory, C & ME, NIU, approved by Taiwan’s Ministry of Science and Technology (MOST 106-2221-E-197-020-MY3, 106-2923-E-197-002-MY3, 106-2621-M-197-001, 105-2622-E-197-012-CC3).