Title Page

Abstract

Contents

Definition of Acronyms 11

Ⅰ. Introduction 15

1.1. Background 15

1.1. General overview of energy in DR Congo 19

1.2. Electricity issue and waste management issue in Kinshasa city 21

1.2. Problem Statement 24

1.3. Objectives and Goals 26

1.4. Research Question 27

1.5. Impact and Significance of the Study 27

1.6. Organization of the research 27

Ⅱ. Theoretical Framework and Literature Review 29

2.1. Municipal Solid Waste 29

2.1.1. Solid waste Definition 29

2.1.2. Solid waste generation 30

2.1.3. Waste Classification 31

2.1.4. Solid Waste Disposal Management 32

2.1.5. Municipal Solid Waste 35

2.1.6. MSW Management 35

2.1.7. Physical and Chemical Composition of MSW 37

2.1.8. Valorization of solid waste 42

2.2. Waste to Energy technology 43

2.2.1. Incineration 46

2.2.2. Pyrolysis 47

2.2.3. Gasification 48

2.2.4. Comparative study of Waste to energy technologies 50

2.3. Plasma technology 51

2.3.1. Definition 52

2.3.2. Plasma production 52

2.3.3. Type of plasma torches 52

2.3.4. PE-IGCC (Plasma Enhanced Integrated Gasification Combined Cycle) 53

2.3.5. Arc plasma and PE-IGCC comparison 54

2.3.6. Importance of plasma technology 55

2.4. Internet of Things (IoT) 55

2.5. Machine Learning 56

2.5.1. Definition 57

2.5.2. Type of learning 57

2.5.3. Deep learning 60

2.5.4. Model Validation 65

2.6. Possible solution 68

2.7. Previous research 69

Ⅲ. Methodology 71

3.1. Research design 71

3.2. Data collection 73

3.2.1. Primary data acquisition process 73

3.2.2. Secondary data acquisition 75

3.3. Proposed solution system and explanation 75

3.3.1. Detailed proposed solution 77

3.3.2. Summarized proposed energy generation and distribution system 78

3.3.3. MSW Sorting system based on IoT Sensors 79

3.3.4. Power supply system for plasma energy's generation 80

3.4. Features analysis 81

Ⅳ. proposed Plasma Enhanced-Integrated Gasification Combined Cycle using MSW with Smart grid system Result and discussion 84

1. Study of chemical and thermal characteristics and quantity of MSW and current energy distribution system analysis in Kinshasa city 84

1.1. Physical composition of Waste determination in Kinshasa 84

1.2. Proximate and Ultimate Analysis in Kinshasa city 87

1.3. Current and proposed energy distribution system analysis 90

2. Feasibility study to apply Hybrid plasma technology to minimize the impact of bad waste management system while generating power. 91

2.1. Simulation model using Aspen plus software 91

2.2. Determination of the Syngas available from the Aspen plus Gasification simulation 95

2.3. Estimation of energy production 98

3. Automatic control system (Smart grid), based on Machine learning and IoT for an optimal power distribution result. 100

3.1. Data collection and Preprocessing 100

3.2. Split dataset into train and test set 105

3.3. LSTM model training 106

3.4. Model Evaluation and interpretation 107

3.5. Model result interpretation 110

3.6. Machine learning Model prediction 110

3.7. IoT devices determination 112

3.8. Smart grid system architecture 112

4. Proposed solution of power plant implementation feasibility study 114

1.1. Capital Investment 114

1.2. Cost of construction 115

1.3. Estimated revenue 116

1.4. A summary of the stand-alone small scale plasma power plant 116

5. Discussion 117

Ⅴ. Conclusion and Recommendation 122

Reference 125

APPENDIX 135

Action plan 136

0. Mission 136

1. Scope and Advantage 136

2. Duration of the action plan 136

3. Expected outcomes 136

4. Evident of success 137

5. Activities 138

6. Constraints 140

7. Activities Dependency 141

8. Activities and Deadline 142

Table 1. Factors influencing MSW generation rate 31

Table 2. Physical Classification of MSW 39

Table 3. Comparative study of WtE technologies 50

Table 4. Comparison between Westinghouse arc plasma and green science hybrid IGCC 55

Table 5. Cleanest energy source comparison 68

Table 6. Energy forecast system comparison 69

Table 7. Previous or related work 70

Table 8. Respondents interviewed list 74

Table 9. Microwave plasma torch vs Arch Torch 82

Table 10. Total Percentage of MSW Composition in Kinshasa 86

Table 11. Percentage of MSW Composition in Kinshasa 87

Table 12. Proximate Analysis 88

Table 13. Ultimate Analysis 89

Table 14. Block of operation description 92

Table 15. Syngas composition result 97

Table 16. A summary of total annual cost 116

Table 17. Summary of total income 116

Table 18. A summary of the stand-alone small scale plasma power plant 117

Table 19. SWOT Analysis of PE-IGCC in Kinshasa city 121

Table 20. Constraint of power plant project implementation 141

Table 21. Activities dependency 141

Table 22. Action plan 143

 Electricity is a critical resource in the daily life of a population, industry, business, or any type of organization since it enables the investments, innovations, and new sectors that drive job creation, inclusive growth, and shared prosperity across entire economies (World Bank, 2022). There is no development without electricity because many sectors of life deal with it.

Congo, the Democratic Republic of, is one of the largest Countries around the world, second in Africa, and in addition, the richest country in terms of natural resources with a population estimated at around 90 to 100 million inhabitants. However, it is among of low electrification rates country in the world less than 20% of the Congolese population has access to electricity.

In the same line of thought, every day, DR Congo produces a huge quantity of solid waste generated in the country without a suitable management system. It is widely accepted that the primary causes of waste generation are urbanization, economic growth, and population growth. Kinshasa metropolises city produces around 11900 tons of MSW per day. Taking out all kind of waste could not be used for energy production, the estimated amount of waste is around 451.21 tons from 24 of municipalities, with production average of 208.41 tons each township. This situation raises concerns about the long-term management of waste capacity. Besides this, the absence of an effective management system results in flooding, air pollution emissions from household waste burning, and an increase in the number of mosquitos, causing public health concerns.

Furthermore, Solid waste could be used as a source of energy and that power generation from waste can play an important role in reducing the impacts of municipal solid waste (MSW) on the ecosystem. In addition, various methods currently accomplish waste management including incineration, landfills, biochemical conversion, and others. However, among of these processes, some are not environmentally friendly because waste reduction efficiency is low and causes health risks. Under this situation, appropriate and sustainable environment waste-to-energy conversion technologies are critical for attempting to address the massive amount of Solid waste produced and energy trustworthiness sustainable and responsible, resulting in the transformation of a habitable environment.

The objective of this research focused on a suitable electricity production source while automatically ensuring energy balance between production source and distribution throughout a smart grid on one hand and providing a new way or technique to overcome the shortcomings of current solid waste treatment methods to solve environmental pollution and its consequences on man in another hand.

Thereby, the study examined the feasibility of the implementation of hybrid plasma technology through a gasification process as a suitable or eco-friendly waste-to-energy technology in the city of Kinshasa. The physical composition analysis in Kinshasa city was conducted and findings show that the city has the diverse type of waste depending on the season.

In accordance with the findings, Aspen plus software was able to produce Syngas from the simulation and the result show Mass fraction of H2, N2, H2O, CO, CO2, CH4 with a mass density of 0.37 kg/cum. Thereby, the expectation capacity to install from this pilot project is about 11.82 MW/h PE-IGCC power plant. In addition, from this capacity of energy produced using plasma gasification, Machine learning model, especially LSTM RNNs and Simple RNN for automatically learning features from sequence data which support multiple-variate data for multi-step forecasting provided a best automatic control system for energy forecast between a residential and industrial area based on data from Kaggle considering day and nighttime. The result of the ML model shows a best performance of learning with 96% of accuracy.