Dataset for residual concrete reuse and valorization under a circular economy approach in a ready-mix concrete plant
Description
This dataset supports the analysis of residual ready-mix concrete generation, its fresh-state reuse, its recovery as recycled aggregate, and the final non-valorized waste management under a circular economy framework. The data is structured into two main parts: Empirical Baseline Data: Provided directly by an anonymized Peruvian ready-mix concrete plant, including a comprehensive Data Dictionary, Monthly Production records (produced vs. delivered volumes), and Monthly Material Consumption (actual vs. standard standard indicators for sand, stone, cement, and admixtures) spanning from January 2024 to December 2025. Simulation and Scenario Modeling: Developed by the authors based on the empirical baseline. It features aggregated values reported in the main manuscript, discrete-event simulation model parameters configured in Simio software (using continuous probability distributions such as Normal and Triangular for order arrivals, volumes, and residual rates), experimental scenario designs (Baseline, Conservative, Intermediate, Optimistic), and the corresponding mean simulation results (valorization and discard rates) evaluated over a simulation horizon of 3,485 operating hours (410 operating days).
Files
Steps to reproduce
To reproduce the results, first locate the 'Monthly_production' and 'Monthly_materials' sheets. Compute the historical baseline by comparing actual raw material consumption against standard mix designs for the 2024–2025 period, applying the waste percentage formula to verify the overconsumption gap, which averages an overall waste generation rate of 5.32%. Next, recreate the discrete-event model in Simio by designing a network with a Source (Source_Pedidos), two sequential processing Servers (ProduccionConcreto and EvaluacionResidual), a multi-branch proportional Transfer Node, and specialized downstream pathways leading to two Sinks (Sink_Valorizado and Sink_NoValorizado). Set the simulation horizon to 3,485 operating hours (410 days at 8.5 h/day), configure 10 independent replications per experiment, and set the batch entity arrival interval rate at an average of 2.5 hours. Program the stochastic process logic in the process editor (P_CalcularResidual) linked to the entity state assignments as follows. Assign the state variable ModelEntity.a_VolProducido using the expression 'Math.Max(0.5, Random.Normal(7.12, 1.04))' to represent the normal distribution of order sizes bounded by a minimum operational constraint of 0.5 m³. Assign ModelEntity.a_TasaResidual using a continuous triangular distribution 'Random.Triangular(0.03, 0.0532, 0.08)', where 0.0532 is the empirically calibrated mode. Compute the residual volume per entity with the expression 'ModelEntity.a_VolResidual = ModelEntity.a_VolProducido * ModelEntity.a_TasaResidual' and accumulate these into the global variables v_VolProducidoTotal and v_CRG. Configure the proportional distribution algorithm at the decision node to split entity flows by mapping a random number (range 0 to 1) to the allocation percentages in the 'Scenarios' tab. Route A (RutaA_Moldes) directs fresh workable concrete to molds, computing net recovered volume (VRF) by multiplying input volume by the scenario's Route_A_Performance (e.g., 95% for the intermediate case). Route B (RutaB_Endurecimiento, RutaB_Trituracion, RutaB_Zarandeo) routes hardened material through curing, jaw crushing, and screening, computing net aggregate volume (VAR) using the Route_B_Performance (e.g., 80% for the intermediate case). Route C (RutaC_RegistroDescarte) sends contaminated material straight to Sink_NoValorizado at a 100% loss yield. Finally, run the simulation across the 4 pre-programmed scenarios (Base, Conservative, Intermediate, Optimistic), extract the mean responses from the Simio Pivot Grid, and evaluate indicators using the control balance equation 'Balance = CRG - (VRF + VAR + VNV)' to guarantee zero volumetric leakage.
Institutions
- Peruvian University of Applied SciencesLima Province, Lima