Full-scale production of high-quality wood pellets assisted by multivariate statistical process control

Abstract

In order for solid biofuels to be marketable and accessible to stakeholders (e.g., manufacturers, transhippers and customers), they must strictly meet the rigors of international standards and regulatory agencies regarding quality. On this basis, this study assesses the production of high-performance fuel-flexible woody pellets for heating and power, with the assistance of multivariate statistical process control (MSPC). The operational scenarios of industrially pelletizing pinewood sawdust are POSI (150 MPa and no pre-heating) and POSII (250 MPa and no pre-heating) as references, and POSIII (150 MPa and pre-heating) and POSIV (250 MPa and pre-heating) as alternatives to regularize the workflow on the machine. The stream-monitoring techniques of MSPC are the exponential moving average (EMA) and Hotelling's T2. The EMA-Hotelling system accurately tracks non-random errors and predicts the operationally finest scenario for developing high-quality pellets. Pre-heating the compressing channel of the machine regularizes the feeding and thus produces type I pellets (class A1/A2, European standard) with excellent bulk density (1227.55 kg m−3), durability (98.10%) and hydrophobicity (96.65%), without points outside the specific critical ranges. Clear evidence of improved pellet quality with the assistance of MSPC is found. The high-fidelity concept of MSPC captures the advantages of EMA and Hotelling's T2 into an immersive single framework; hence, it can compensate for the potential bias and misinformation-to-information overlapping of outcomes from monitoring latently multivariable flow on classical quality control charts. The major findings and innovations of this study can assist with rolling-out fine-scale pelletization and thus fulfill the increasing global demand for high-quality biofuels.