MANAGEMENT DECISION SUPPORT SYSTEM OF SOLVENT-BASED POST-COMBUSTION CARBON CAPTURE

A management decision-support framework for a coal-fired power plant with solvent based post combustion CO2 capture (PCC) (integrated plant) is proposed and developed in this thesis. A brief introduction pertaining to the solvent-based PCC technology, thesis motivations and objectives are given in Chapter 1. Chapter 2 comprises a comprehensive literature review of solvent-based PCC plant from the bottom level (PCC instrumentation level) until the top level (managerial decision of PCC system). Chapter 3 describes the development of solvent-based PCC dynamic model via empirical methods. Open-loop dynamic analyses are presented to provide a deeper understanding of the dynamic behaviour of key variables in solvent-based PCC plant. Chapter 4 presents the design of the control architecture for solvent-based PCC plant. Two control algorithms developed, which utilise conventional proportional, integral and derivative (PID) controller and advanced model predictive control (MPC). Chapter 5 proposes a conceptual framework for optimal operation of the integrated plant. The MPC scheme is chosen as the control algorithm while mixed integer non-linear programming (MINLP) using genetic algorithm (GA) function is employed in the optimization algorithm. Both algorithms are integrated to produce a hybrid MPC-MINLP algorithm. Capability and applicability of the algorithm is evaluated based on 24 hours and annual operation of integrated plant. Chapter 6 extends the scope of Chapter 5 by evaluating the relevance of solvent-based PCC technology in the operation of black coal-fired power plant in Australia. This chapter considers a prevailing climate policy established in Australia namely Emission Reduction Fund (ERF). Finally, the concluding remarks and future extensions of this research are presented in Chapter 7

The emergence of phase change material in solar thermal energy: A scientometric review

Phase change material (PCM) features an attractive option due to its solar thermal storage capability to assist the cooling/heating process especially during night operation, thus contributing to the reduction of energy cost and carbon footprint. This study aims to analyse the emergence of PCM in the application of solar thermal energy. Subsequently, to envisage Technology Readiness Level (TRL) and commercialisation opportunity based on historical and contemporary research trends. This review encompasses of peer-reviewed literatures from Scopus database for one decade between 2010 and 2019. Based on the review, there is a moderate growth on the research related to PCM-solar thermal at 22% of emergence rate from the past one decade. China has dominated in this research development by concurring approximately 22% from the number of research articles published globally. It can be concluded that the application of PCM in solar thermal energy system is at TRL 5 which reflects research and development (R&D) progress is at intermediate prototypical development based on the trend of academic publication. Furthermore, based on the review, PCM features great potential in commercialisation opportunity due to its vital contribution as a frontier material/substance in overcoming the challenges of energy and environmental insecurity

Waste-energy-climate nexus perspective towards circular economy: a mini review

Population growth requires high demand for vast resources, which includes energy and the consumption of goods. With this strangling situation, many developing countries – including Malaysia; need to face other associated issues such as waste generation as well as climate change. To survive this daunting scenario, a smart alternative known as Waste-to-Energy (WtE) technology emerges as a viable solution to overcome these contemporary challenges. By implementing this technology, waste can be transformed into new energy sources that can be used to meet the needs of the standard energy requirement, whilst, solving mass pollution problems. Though it may sound like a promising solution as of this moment, the availability of these resources and the longevity of this technology is relatively unknown. A mini-review of the modern nexus of waste and energy with the value-added of climate change may enhance the understanding of this linkage and forecast its long-term sustainability. Additionally, this review features an innovation compares with the mainstream reviews by evaluating the nexus of these three in line with the Circular Economy framework (CER). This review encompasses a body of grey-literatures and peer-reviewed literature from multi-disciplinary perspectives including government, researcher, and public. Information obtained in this study exhibits a significant linkage between waste utilization, climate change, and energy security. Identified implications and effects of each provide vital insight towards the establishment of national and global CER. Apart from that, WtE technology turns out to be the best effort as part of the waste minimization strategy and serves as a key factor that can attenuate the environmental impacts concurrently meeting the growing demand of national and global energy

Economic and environmental sustainability of low carbon power generation: relevancy in Malaysia green technology master plan (GTMP)

The Malaysia Green Technology Master Plan (GTMP) is a systematic framework and planning initiative to mitigate national CO2 footprints by targeting several sectors which include power generation (energy sector). Thus, the objective of this study is to analyze economic and environmental sustainability (via techno‐economic analysis) of CO2 mitigation technology (i.e. postcombustion CO2 capture system, PCC) that can be employed by the energy sector as a way forward for low‐carbon power generation. The analysis was performed by using a previously developed hybrid mixed integer nonlinear programming and model predictive control (MINLP‐MPC) algorithm. Based on the simulation results, operation of coal‐fired power plant associated with PCC plant under fixed forecast conditions (year 2020) would be capable of delivering substantial net operating revenue, subject to carbon taxes at US$7, 10 and 14 t–1 CO2 (scenarios 1–3), with the highest revenue gained from Scenario 1 (US$7 t–1 CO2) of US$601 million yr–1. Contrariwise, PCC plant operated at minimum efficiency by only capturing c.24% from the total CO2 emissions (under three carbon tax prices), illustrating the ineffectiveness of this plant for promoting national environmental sustainability. The big trade‐off between economic and environmental sustainability found in this study demonstrates the impracticality of PCC technology as a mitigation measure in pursuing low‐carbon power generation as part of Malaysia's GTMP

Investment decision making for carbon capture and storage technology in high efficiency, low-emission coal-fired power plant via dynamic techno-economic-policy evaluation framework: Case study in China

There are significant uncertainties pertaining to the future of coal power generation across the globe and especially for China. Being one of the largest coal consumers and CO2 emitters, China aims at addressing its national energy security while ensuring to achieve sustainable operations. In following its commitments to the Paris Agreement and in pursuit of near-zero emissions from power plants, China has actively moved towards high efficiency, low emission (HELE) coal-fired power plants. However, HELE technology alone cannot completely cut carbon emissions, and will require a mature CO2 emissions reduction technology such as carbon capture and storage (CCS) to significantly reduce the CO2 emissions within short to medium term. This paper conducts a preliminary evaluation for investment in HELE coal-fired power plant with CCS (HELE-CCS) in China, under the context of the Shenzhen emissions trading scheme. We carry out this preliminary analysis through mixed-integer non-linear programming (MINLP) framework, and by using publicly available power generation and carbon pricing data. Based on hypothetical scenarios, a HELE coal-fired power plant is found to contribute 55 % profit by selling the electricity. However, a substantial CO2 emissions cost is incurred due to the installation of CCS, leading to a high caution on investment. A more detailed analysis is warranted using real power plant data to confirm real business feasibility of HELE-CCS, including an expansive set of scenarios that also assess relevant appropriate government incentives and subsidies as well as variant financial models

Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties

This paper employs a control-based optimization algorithm encompassing of an intelligence model predictive control (MPC) scheme and mixed integer non-linear programming (MINLP) for coal-fired power plant retrofitted with flexible solvent-based post combustion CO 2 capture (PCC) plant (integrated plant). The agility and robustness of the developed control algorithm (MPC) is demonstrated through the control response time and efficiency of energy requirement including the financial and operational benefits of the plant subjected to plant and market uncertainties. While, the MINLP is utilized to forecast plant operational modes by ensuring the operational fidelity of integrated plant. This involves utilization of historical (2011) and forecast (2020) market conditions (electricity tariff and carbon price) subject to maximum plant net operating revenue. The outcomes show that the future power plant will operate in mixed operation modes, for instance in unit turndown and load following modes, which contribute to a minimum capture energy penalty at 3.13 MJ th /tonne CO 2 . Moreover, under the same year (2020), MPC exhibits superior control performance by satisfactorily obtain 94% actual CO 2 capture from the ideal cumulative CO 2 capture. Additionally, the integrated plant is capable to resume approximately 96% actual revenue from the ideal net operating revenue projected by the control-based optimization algorithm. The algorithm demonstrates that the installation of control system package (MPC) into the flexible PCC plant associated with coal-power generator could contribute to efficient energy management subjects to unprecedented uncertainties

An intelligent platform for evaluating investment in low-emissions technology for clean power production under ETS policy

This study develops an investment decision making platform for carbon capture and sequestration (CCS) technology using an artificial intelligent (AI) algorithm featuring an optimization via a mixed integer non-linear programming (MINLP) formulation. This computational strategy offers a smart rapid investment decision evaluation of CCS technology through several economic-environmental-technical-policy (EETP) uncertainties. This is applied to a coal-fired power plant (PP) in Shenzhen, China. Historical (2019) and forecast (2030) operations are evaluated under dynamic and static carbon price regimes. Scenario 1 under dynamic carbon pricing exhibits a positive (sustainable) investment decision for CCS deployment at 28% net revenue gain of selling electricity. Scenarios 2–4 feature negative (unsustainable) investments for CCS technology at 44%, 7% and 66% net revenue loss, respectively. Carbon price is identified to be the dominant variable/uncertainty in recognizing the sustainability outcome of CCS investment followed by the combined market trends of coal and electricity prices. This current work demonstrates a computation approach for dealing with all the uncertainties at hand and is therefore necessary and critical for rational future investment decisions and operations in clean power production (as demonstrated in this PP + CCS context), suggesting the EETP objectives cannot be met without intelligent algorithmic operations. The present analysis exemplifies the trade-offs mainly between the cost of CO2 emission and the cost of PP operation with CCS. It can be used as an indicator on the energy transformation readiness based on current and forecast global conditions. This algorithmic approach can be generalized and extended to other cleaner power production processes and to alternative energy-based industrial symbiosis (IS), which collectively aims to mitigate the use of traditional fuel (i.e. coal) and subsequently stimulating a circular economy energy transition

Relevancy of Emission Reduction Fund (ERF) policy towards large-scale deployment of carbon capture technology in black coal-fired power plant

This study implements a single objective multi-constrained optimisation technique to evaluate the relevance of large scale deployment of post-combustion CO2 capture (PCC) technology as an emissions reduction fund (ERF) ‘project’ for black coal power generation in Australia. We target maximum net operating revenue, by generating forecasts of power plant load and CO2 capture rate, and while subject to the dual operational and environmental constraints. Four different hypothetical Australian Carbon Credit Unit (ACCU) prices ($AU 5, 15, 25 and 50/tonne CO2) are evaluated, through a single objective multi-constrained optimisation algorithm, for a 7-year contract period between 2016 and 2020 and with a 7.1 MT CO2 of emission baseline. The results indicate that at ACCU price of$AU 25/tonne CO2 (Scenario 3), represents a feasible solution for future deployment of PCC technology under ERF project settings. Across the contract period, PCC plant captures 90% CO2 from the power plant emissions with total plant net operating revenue at approximately $AU 1,765 million. The gross revenue gains from ERF incentive and selling of electricity are at 25% and 75% respectively. These findings point to the value of this computational approach for power plant operators in Australia considering low emissions technologies (viz. PCC) as ERF projects, aiding in their short and medium-term planning. Such an approach is extendable to other countries and regions under varying emissions trading schemes

Semi-quantitative chemical expert tool for occupational safety and health (use and standards of exposure of chemicals hazardous to health) regulations 2000

Occupational Safety and Health (Use and Standards of Exposure of Chemicals Hazardous to Health) Regulations 2000 (USECHH 2000) is a regulation under the Occupational Safety and Health Act (OSHA) 1994 that has been established to provide a legal framework for controlling the exposure of chemicals hazardous to health (CHTH) at the workplace. The increasing number of occupational diseases and poisonings reported is worrying as it reflects improper chemical safety management and non-compliance with relevant legislations. The lack of systematic techniques that could be easily adopted for self-assessment has hindered many organizations from complying with this regulation. To help curb this issue, a semi-quantitative self-assessment tool, called chemical expert tool (CET), was developed based on USECHH 2000 and Guidelines on ontrol of Chemicals Hazardous to Health, 2001 (GCCHTH 2001). The CET was developed using Microsoft Excel and validated via feedback from four expert panels and three case studies. The case studies were carried out involving three end-users to experience the usability, comprehensiveness, and efficiency of the CET. At the end of the self-assessment, the users were provided with the value of the percentage of compliance with USECHH 2000 and GCCHTH 2001, together with the ability to identify the area for improvement, which in the end could lead to better compliance and a safe and healthy workplace. Based on the responses to all questions in the system usability scale, most of the respondents were satisfied with the CET. Utilizing the CET will help the users promote and provide safe management of CHTH at the workplace