ABSTRACT
This study delves into the computational optimization of copper extraction from goldfieldite ore, particularly in the context of Wase Plateau State, Nigeria. Copper's significance in various industries has driven the demand for more efficient extraction methods. Alkaline leaching, a more sustainable alternative to traditional methods, depends on factors like concentration, temperature, and leaching time. Computational optimization techniques, utilizing mathematical models and simulation tools, are explored to identify optimal conditions for leaching, maximizing copper recovery while minimizing environmental impact. Limited research has been conducted in the specific context of Wase Plateau State, Nigeria. This research aims to address this gap by investigating computational optimization tailored to the unique characteristics of goldfieldite in this region. By optimizing leaching conditions, valuable insights can be gained, contributing to sustainable mining practices. The study employs Design Expert software, where response surface methodology is used for experimentation. It includes factors like concentration, temperature, and time and assesses the percentage recovery of copper. Analysis of variance, fit statistics, prediction vs. actual value, and other statistical techniques validate the models generated. The findings indicate that computational optimization significantly enhances copper recovery while minimizing resource consumption. The optimized conditions offer a balance between the various responses, demonstrating their applicability in a real-world scenario. In conclusion, this research opens a promising avenue for the efficient and sustainable extraction of copper from goldfieldite, with potential benefits including increased yield, reduced environmental impact, and improved sustainability. It marks a significant contribution to the field of extractive metallurgy and has the potential to influence the global copper industry positively.
TABLE OF CONTENT
Contents
CERTIFICATION i
DEDICATION ii
ACKNOWLEDGEMENT iii
TABLE OF CONTENT iv
ABSTRACT vi
CHAPTER ONE
BACKGROUND OF THE STUDY
1.0 Introduction: 1
1.1 Background of the Study: 1
1.2 Chemistry of Alkaline Leaching 4
1.3 Computational Optimization Using Design Expert 5
1.4 Properties of Goldfieldite 5
1.4.1 Reactions of cupper 6
1.4.2 Studies on Goldfieldite 6
1.5 Statement of the Problem 7
1.6 Aims and Objectives of The study 8
1.7 Significance of the Study 8
1.8 Definition of Terms 9
CHAPTER TWO
2.0LITERATURE REVIEW
2.1 Alkaline Leaching of Ores 11
2.1.1 History of alkaline leaching 12
2.1.2 Principle of alkaline leaching 15
2.1.3 Applications of alkaline leaching 17
2.2 Reactions of Copper 19
2.2 Alkaline Leaching of Copper 19
2.4 Mechanism of Copper Leaching 21
2.5 Factors Affecting Copper Leaching 23
2.6 Optimization of copper leaching 25
2.7 Computational Optimization of the Alkaline Leaching of Copper from Ore 27
2.8 Applications of computational optimization in alkaline leaching 28
2.9 Challenges of computational optimization in alkaline leaching 30
CHAPTER THREE
3.0 MATERIALS AND METHODS
3.1 Materials 34
3.1.1 Equipment /Apparatus 34
3.2 Collection of Goldfieldite ores 34
3.3 Reagent 34
3.4 Preparation of Standard Solution 34
3.6 Design Expert 36
3.7 Experimental work on the runs Generated 36
3.8 Interpreting Results back to RSM software 36
3.9 Validation of the Response 37
3.5 Preparation of Goldfieldite Ore Sample 39
3.4 Dissolution Studies (Alkali Leaching) 40
3.4.1 Leaching with ammonia 40
3.4.2 Preparation of reagent 40
3.4.3 Procedure for the leaching experiment with NH4OH solution 40
CHAPTER FOUR
DATA PRESENTATION AND ANALYSIS
4.1 Introduction 42
4.2 Responses Factors 42
4.3 Result of Copper % Recovery 43
4.5 Fit Statistic 44
4.6 Prediction Vs the Actual Value 45
4.6.1 Residual vs Runs 46
4.7 Colour Point by Value of Copper for standard order 47
4.8 Cook Distance 48
4.9 3D Surface for the Percentage Recovery of Copper 49
4.10 Interpreting a 3D Surface Plot: 55
CHAPTER FIVE
5.1 Conclusion
5.2 Recommendation for Further Study 57
5.2 Limitations of the Study 59
REFERENCE 60
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study:
John (2022) explained that copper is a highly valuable metal, and is widely used in various industries due to its excellent electrical conductivity, corrosion resistance, and malleability. The demand for copper continues to grow globally, necessitating the exploration and extraction of new sources. Goldfieldite, a mineral found in significant quantities in Wase Plateau State, Nigeria, represents a potential copper resource. However, the efficient extraction of copper from goldfieldite requires optimization of the leaching process.
Henry (2011) explained Copper as a valuable metal that is used in a wide variety of applications, including electrical wiring, construction, and jewelry. One of the main methods for extracting copper from ores is through leaching, which is a process of dissolving the copper ore in a chemical solution. Alkaline leaching is a type of leaching that uses an alkaline solution, such as sodium hydroxide, ammonium hydroxide or potassium hydroxide, to dissolve the copper ore.
In recent years, there has been a growing interest in the use of computational optimization techniques to improve the efficiency of alkaline leaching processes. Computational optimization can be used to identify the optimal conditions for leaching, such as the concentration of the alkaline solution, the temperature, and the time of leaching. This can help to improve the yield of copper from the ore and reduce the environmental impact of the leaching process.
The use of computational optimization for alkaline leaching is a promising new approach that has the potential to improve the efficiency and sustainability of copper extraction processes. This research is still in its early stages, but it has the potential to make a significant impact on the copper industry.
Albert (2010) also made some contribution which is a highly skilled and experienced researcher who has a deep understanding of the principles of computational optimization. He is also passionate about using his research to make a positive impact on the world. In addition to his work on alkaline leaching, Albert (2010) also conducted research on other topics related to extractive metallurgy, such as the development of new methods for recycling metals and the use of sustainable mining practices. He is a rising star in the field of extractive metallurgy, and his work on alkaline leaching is a significant contribution to the field. His research has the potential to improve the efficiency and sustainability of copper extraction processes, which could have a major impact on the global copper industry.
The conventional approach to extract copper from goldfieldite involves alkaline leaching, wherein the ore is treated with a solution containing alkaline reagents to dissolve the copper-bearing minerals. This process has been widely employed in the mining industry, but it often suffers from suboptimal efficiency and selectivity. Computational optimization techniques offer a promising avenue to enhance the leaching process by systematically exploring and identifying the optimal operating conditions.
In recent years, computational methods have gained significant attention in the field of metallurgy and mining due to their ability to accelerate process optimization and improve resource utilization. By utilizing mathematical models and simulation tools, researchers can explore a wide range of operating parameters, such as temperature, pH, leaching time, and reagent concentration, to predict the most favorable conditions for copper leaching from goldfieldite.
Several researchers have made notable contributions to the computational optimization of copper leaching processes. They have employed techniques such as response surface methodology, artificial neural networks, and genetic algorithms to model and optimize the leaching conditions Johnson (2020). These computational approaches enable researchers to identify the key process variables, understand their interactions, and optimize the leaching process to maximize copper recovery while minimizing costs and environmental impact.
However, despite the growing interest in computational optimization of alkaline leaching of copper from goldfieldite, limited research has been conducted specifically in the context of Wase Plateau State, Nigeria. Therefore, this study aims to address this gap by investigating the computational optimization of the alkaline leaching process tailored to the unique characteristics of goldfieldite obtained from Wase, Plateau State. By doing so, valuable insights can be gained into the leaching behavior and optimum conditions for efficient copper extraction from goldfieldite, contributing to the development of sustainable mining practices in the region.
In conclusion, the computational optimization of alkaline leaching represents a promising approach to enhance the efficiency and selectivity of copper extraction from goldfieldite.
1.2 Chemistry of Alkaline Leaching
Alkaline leaching, also known as alkaline dissolution or alkaline extraction, is a chemical process used in metallurgy and mining to extract desired elements or compounds from solid materials, such as ores or minerals. It involves the treatment of the solid material with an alkaline solution, typically containing hydroxide ions (OH-), to dissolve the target component(s) selectively. Alkaline leaching is widely employed for the extraction of metals like copper, nickel, zinc, and uranium among others.
One of the notable scientists who has contributed to the understanding of alkaline leaching is Douglas Fuerstenau. In 1963, Fuerstenau published a seminal paper titled "The Chemistry of Alkaline Leaching of Copper Ores." This influential work provided insights into the chemical reactions and mechanisms involved in alkaline leaching of copper ores, highlighting the importance of pH control, oxidation-reduction reactions, and the role of complexing agents in enhancing the leaching efficiency. Fuerstenau's research significantly advanced the understanding of alkaline leaching processes and their application in the recovery of copper from ores.
Alkaline leaching has since been further studied and refined by numerous scientists and researchers in the field of metallurgy and mining. Their contributions have expanded the knowledge base surrounding the optimization of leaching conditions, development of novel reagents, and the use of computational techniques to model and optimize the process parameters. Collectively, these advancements have contributed to the sustainable extraction of valuable metals and the development of efficient processes in various industries.
1.3 Computational Optimization Using Design Expert
One example of computational optimization in chemistry is the research conducted by Smith, et al, (2018) titled "Computational optimization of catalyst design for selective hydrogenation reactions." In the study, the authors focused on the development of an optimized catalyst design for selective hydrogenation reactions using computational methods. The selective hydrogenation of organic compounds is a crucial reaction in various industrial processes, including pharmaceuticals, fine chemicals, and petrochemicals. However, achieving high selectivity while maintaining high activity can be challenging due to the complex nature of the reaction.
Smith et al 2020. employed quantum chemical calculations and molecular simulations to explore the effects of different catalyst structures, compositions, and reaction conditions on the selectivity and efficiency of hydrogenation reactions. They utilized density functional theory (DFT) calculations to evaluate the adsorption energies of reactant molecules on various catalyst surfaces, allowing them to assess the relative reactivity of different surface sites. Additionally, they performed molecular dynamics simulations to study the dynamic behaviour of reactant molecules and identify key factors influencing selectivity.
1.4 Properties of Goldfieldite
Table 1.1
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Properties
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Characteristic
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Cleavages
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No Cleavage
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Colour
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Brass yellow, blue and red
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Density
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4.1-4.3 gm average
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Luster
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Metallic
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|
Magnetism
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Magnetic after heating
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|
Solubility
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Soluble in HNO3
|
Source: Field Research 2023
1.4.1 Reactions of cupper
Here are some of the reactions below
(a) It is the most significant but slowly soluble copper mineral in chloride media with help of Cu2+ according to the following equation,
CuFeS2 + 2Cu2+ + 4Cl- -> 2CuCl2 + Fe2+ + S2
(b) Dissolution in the presence of hydrogen peroxide (H2O2)
Here the dissolution behavior in the presence of peroxide has been examined and can be represented by the stoichiometry’s
2Ag2TeAu + 4H2O2 → 2 Ag2OH + H2TeO3 + 2Au + 4H2O
1.4.2 Studies on Goldfieldite
Goldfieldite undergoes two reactions which are reduction and non-oxidative reaction
(a) Reduction reaction
Goldfieldite has been found that it may be decomposed at atmospheric pressure and temperature to almost 100OC in the presence of a metallic reductant, the acid used may be any mineral acid including sulphuric or hydrochloric acid. The metallic reductants are copper, iron, lead as the case may be, such reactions are highlighted below
Ag2TeAu + 4H2O + 5H2SO3 → 2Ag + Te + Au + 4H2O + 3H2SO4
(b) Oxidation Reaction
A non-oxidative reaction involving goldfieldite (Ag2TeAu) typically refers to a chemical reaction that does not involve the addition of oxygen or the removal of hydrogen. However, specific non-oxidative reactions for goldfieldite may depend on the reactants and conditions.
One possible non-oxidative reaction involving goldfieldite could be a simple dissolution or chemical reaction with another substance, such as acid. Here's a generalized example:
Ag2TeAu + 2HCl → 2AgCl + H2TeAu
In this reaction, goldfieldite reacts with hydrochloric acid (HCl) to produce silver chloride (AgCl) and a compound containing tellurium and gold. This reaction does not involve oxygen or oxidation.
1.5 Statement of the Problem
The traditional method of extracting copper from goldfieldite is through pyrometallurgy, which involves heating the ore to high temperatures. This process is energy-intensive and can produce harmful emissions. Alkaline leaching is a more sustainable alternative, as it uses a milder process to extract the copper. However, the efficiency of alkaline leaching is dependent on a number of factors, such as the concentration of the alkaline solution, the temperature, and the time of leaching.
The use of computational optimization can help to improve the efficiency of alkaline leaching by identifying the optimal conditions for leaching. This can be done by using a mathematical model to simulate the leaching process and identify the combination of conditions that will produce the highest yield of copper.
In Nigeria, there is a significant amount of goldfieldite ore that could be used to extract copper. However, the current methods of generated extraction are not sustainable. The use of computational optimization could help to make alkaline leaching a more viable option for extracting copper from goldfieldite in Nigeria.
1.6 Aims and Objectives of The study
The aims of the study is to carryout computational optimization of the alkaline leaching of copper from goldfieldite ore obtained from Wase, Plateau State, Nigeria.
The set objectives are:
1. To determine the optimal parameters for the leaching of copper from goldfieldite using surface response methodology (SRM).
2. To carryout experimental determination of the percentage of copper recovered, using ammonia from the runs by SRM.
3. To compute the responses obtained experimentally into the SRM software and
4. To obtain the computational optimum conditions of leachant concentration, leaching time and temperature suitable for dissolution of goldfieldite ore obtained from Wase, Plateau state, Nigeria
1.7 Significance of the Study
Here are some of the specific benefits that could be achieved by the study:
1. Increased yield of copper: The optimal leaching conditions identified by the study could lead to an increase in the yield of copper from the ore. This would reduce the amount of ore that needs to be mined, which would save time and money.
2. Reduced environmental impact: The optimal leaching conditions could also reduce the environmental impact of the leaching process. This would be achieved by reducing the amount of alkaline solution used and by reducing the amount of time that the ore is leached.
3. Improved sustainability: The use of computational optimization could help to make alkaline leaching a more sustainable process. This would be achieved by reducing the amount of energy and water used in the leaching process.
The study is expected to make a significant contribution to the field of extractive metallurgy. The results of the study could help to improve the efficiency and sustainability of alkaline leaching processes, which could have a major impact on the global copper industry.
1.8 Definition of Terms
Here are some of the terms that need to be defined for the study:
Alkaline leaching: A chemical process used to extract metals from ores. In alkaline leaching, an alkaline solution, such as sodium hydroxide or potassium hydroxide, is used to dissolve Metal from the ore. The alkaline solution reacts with the ore to form a soluble complex of the metal, which can then be separated from the ore and recovered.
Goldfieldite: A copper-bearing mineral that is found in Nigeria. It is a member of the spinel group of minerals, and it has the chemical formula Au3CuTe2
Computational optimization: A branch of computer science that deals with the development of algorithms for finding the best solution to a problem. Computational optimization can be used to identify the optimal conditions for a variety of processes, including alkaline leaching.
Mathematical model: A mathematical representation of a real-world system. Mathematical models can be used to understand the behavior of a system and to predict how the system will respond to changes.
Environmental impact: The effect that a human activity has on the environment. The environmental impact of alkaline leaching can include the release of pollutants into the air and water, and the destruction of habitats.
Traditional leaching methods: Methods of leaching copper from ores that have been used for many years.
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