Supplementary data tables for "Efficiency Assessment and Optimization of Oil Sludge Processing and Disposal Systems Using Data Envelopment Analysis"

This dataset contains supplementary materials for the research paper titled "Efficiency Assessment and Optimization of Oil Sludge Processing and Disposal Systems Using Data Envelopment Analysis". The provided files consist of comprehensive data tables (in PDF format) used to evaluate, benchmark, and optimize the operational and environmental efficiency of various oil sludge treatment and disposal systems. The analysis is built upon Data Envelopment Analysis (DEA) methodology, mapping multiple input parameters (such as processing time, reagent mass, fuel/energy consumption, transport costs, and emissions) against output metrics (including mass of recovered products, storage resource value, and eco‑efficiency scores). The dataset includes: Composition and mass data for oil sludge accumulated in storage facilities (Table A1), covering water, hydrocarbons, mineral impurities, and other contaminants across 90+ storage sites. Input and output variables for five DEA models (Models 2–5) that assess resource potential, eco‑efficiency, resource‑energy efficiency, and overall efficiency under both Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) assumptions. Summary statistics (min, max, mean, standard deviation) for all DEA model parameters and efficiency scores (CCR, BCC, and Super‑Efficiency models). Optimization results for the Oil Sludge Processing and Disposal (OSPD) System under four different criteria (resource potential, eco‑efficiency, resource‑energy efficiency, and overall efficiency), presented for varying numbers of processing plants and storage facilities. These tables serve as the empirical foundation for the findings presented in the main text and can be utilized for validation, comparative studies, or further algorithmic optimizations in waste management systems. Researchers and practitioners may also use this data to replicate the DEA models, extend the analysis to other regions, or develop integrated decision‑support tools for industrial waste treatment.