Dataset on the assessment of the environmental, economic and energy parameters of 5 MW CHP co-gasification plant using South African coal, biomass and waste-tyre

The data provided in this article supplements the data information provided in “Techno-economic analysis of electricity and heat production by co-gasification of coal, biomass and waste tyre in South Africa” [1]. The generation of the data considered co-generation of a coal sample (Matla coal) with pine sawdust, sugarcane bagasse, corn cob, and waste tyre at a blend ratio of 1:1, 3:2, and 4:1. The cost evaluation of the use of the feedstocks was considered with feedstock costing (WFC) and without feedstock costing (WOFC). Profitability assessment tools for the case study included NPV, IRR and PBP. The data as contained in this article could be useful for a quick decision making on a similar project by the government and stakeholders in the sector.


Subject area
Electricity and Thermal Power Production More specific subject area Chemical Engineering Type of data Feedstocks of South African origin were milled (size reduction) and sieved for analysis.

Experimental features
Results from the proximate and ultimate analysis was fitted into an empirical model to estimate the LHV of the fuels, and was then used in the model equations shown in the experimental design, material and method section, to determine the annual feed rate and feedstock requirements for the 5 MW electric and thermal power plant. Data source location Johannesburg, South Africa Data accessibility Data are with this article Related research article Techno-economic analysis of electricity and heat production by cogasification of coal, biomass and waste tyre in South Africa [1] Value of the data South African coal reserve depletes very fast, and the CO 2 emission in the country is the highest in the whole of Africa. In this regard, the use of this data article could be instrumental to the reduction in the rapid depletion of the coal reserve, and emissions.
As for as could be ascertained, data describing the electrical and thermal power production in a 5 MW CHP plant using South African feedstock is not available in the open literature; thus, a set of data provided in this article, could be used as a platform for decision making and further R&D in this area.
With the provided dataset, investors can have a good understanding of the techno-economic analysis of the power generation in the plant before embarking on the investment, meaning that the dataset provides a working guide for interested investors.
Policy-making in energy, economic and environmental sectors could consider the dataset, for the modification of existing policies.

Data
The dataset provided in this article supplements the data information provided in [1], recently published in the Journal of Cleaner Production, and it contains 11 tables and 6 figures. Figs. 1 and 2 present the flowchart of the proposed technical approach and the scheme of the 5 MW co-gasification process plant. Tables 1 and 2 are the variation in feedstocks economic parameters at the 10th year estimated with feedstock costing (WFC) and without feedstock costing (WOFC), respectively, and both data were estimated using the lower heating value (LHV) data, moisture content (MC) data and the South Africa and other parts of the globe feedstock prices [1]. Tables 3 and 4 emphasize the results of  the appraisal tools at the 10th year assessment at WFC and WOFC. Tables 5 and 6 indicate the business viability estimation at the 11th year, at WFC and WOFC. The economic evaluation at the 17th year at WOFC is shown in Table 7. Figs. 3-6 depict the comparism of the feedstocks economic parameters obtained at WFC and WOFC, as well as the economic assessment at the 18th year with blend ratio 3:1 and 4:1. Table 8 describes the emissions reduction assessment of the plant via  co-gasification, while Table 9 presents the statistical sensitivity analysis of the statistical estimation showing the mean of the variables amount of feedstock, capital cost investment, cash flows and net present value (NPV), as well as the variance, standard deviation and standard error of the overall evaluations at 10th year, using WOFC.

Experimental design, materials and methods
South African feedstocks comprising coal and solid waste (sugarcane bagasse, corn cob, pine sawdust, and waste-tyre) were pre-treated by milling and sieving and kept in air-tight bags for analysis.    Characterization of the feedstocks was carried out using CHNS-O Organic Elemental Analyzer (FLASH 2000), TGA-SDT (600), Oxygen-bomb calorimeter (203 M 1241) for the ultimate, and proximate analysis and heating value determinations, respectively, and using coal-to-solid waste ratios of 1:1, 3:2, and 4:1. The result from characterization analysis was used in the empirical model (Eq. (1)) to obtain the lower heating value (LHV) of the fuels, and the value was then applied in Eqs. (2) and (3) where LHV and HHV are the lower heating value and higher heating value of the feedstocks in MJ/kg, respectively; F RANNUAL is the annual feed-rate of the plant in t/y; A FR is the annual feedstock requirement of the plant in t/y; η 0 is the operating efficiency of the plant in %, respectively, M H and MC are the mass fractions of hydrogen and moisture content of the feedstocks in %, NOH is the        number of operating hours in the plant in h; ϖ is the energy demand in MWh/y; and γ is the gasification conversion efficiency in %. The economic evaluation of electricity and thermal power generation from the plant was then carried out using the net present value (NPV), internal rate of return (IRR), and payback period (PBP) as the project tools at 10th, 11th, 17th and 18th investment years based on two financial situations namely: with feedstock costing (WFC) and without feedstock costing (WOFC). The appraisal tools and emission models used are provided in Eqs. (4)-(10). The NPV was estimated with Eq. (4) [2]. Eqs. (5) and (6) [2] were applied to estimate the IRR and PBP, respectively. Eq. (7) [3] and Eq. (8) [4] were used to estimate the emissions from the plant. Sensitivity analysis that considers the standard deviation, variance, and standard error was carried out on the variables using Eqs. (9), (10), and (11), respectively [5] NPV ¼ Àβ where NPV, IRR, and PBP are the net present value, internal rate of return, and payback period, respectively; β is the capital investment in ZAR/y; ϕ is the cash flow in million (M) ZAR; R is the annual rate of return in %; T is the economic life of the plant or business period where ξ is the emission reduction by displaced energy from the grid; φ is the effective emission reduction; ε is the life cycle GHG emission intensity of biomass; λ is the emission from transportation of biomass; α is the percentage of feedstock used for the blend; τ is the emission factor of the fuel used SD ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi Variance ¼ ΣðΧÀΧÞ 2 N À 1 ð10Þ SD is the standard deviation of the sensitivity analysis variables; Χ is the variables including the amount of feedstock, capital cost investment, cash flow, and NPV; N is the number of sample population.