Case Study: Shell

. The markov decision algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future. Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, reliable manufacturing, nimble trading, and personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few applications of AI and data science used in the petrochemical industry:

Precision Drilling: Shell is involved in the processing and mining of oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. Based on the historical data from drilling records, and real-time data, the algorithm based on the model is designed to guide the drills as they move through the surface. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to the machinery used.

Efficient Charging Terminals: Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

Monitoring Service and Charging Stations: Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it

Based on the case study above, answer the following questions.

(3,0) Based on the case study described above, what type of machine learning model does Shell use in its prediction analytics process? (3,0)

In this question, provide the following:

(1,0) An explanation of the machine model used in the case.

(1,0) The sentence or description that you identified in the case that justifies the use of the model you are explaining.

(1,0) Two examples of how the machine learning model you are describing works for the case (e.g., like you did in your in-class assignments every week).

Note: Models are not algorithms. Each model can have several algorithms associated with them. Here you need to describe the model.

2 (4,0) Using the case study above, provide two different examples of regression, classification, clustering, and association. In this question, you can use explicit examples identified in the case study description or hypothesize examples (e.g., like you did in your in-class assignments every week).

(1,0) Regression (2 examples):

(1,0) Classification (2 examples):

(1,0) Clustering (2 examples):

(1,0) Association (2 examples)

3 You have a meeting with the CEO of Shell, and you are supposed to convince them to hire you to change the model they are using, which you described in question 1. Your task is to for one of the two other machine learning models available (any other different from the one you identified they are already using, and you described in question 1).

Describe the main benefits of the new model you are suggesting to Shell justifying why the model you are proposing is better than the one they are already using (2,0)