Scheme (mathematics)

Process (computing)

Engineering

Ecology

Biology

EWMA chart

Operating system

Computer science

Statistical process control

Greenhouse gas

Mathematics

Control chart

Electronic engineering

Environmental science

Mathematical analysis

Sensitivity (control systems)

Applied Energy

The industrial sector is considered one of the fastest-growing sources of greenhouse gases, due to the excessive consumption of energy required to cope with the growing production of energy exhaustive products. The statistical process monitoring (SPM) can be an effective tool for monitoring and controlling carbon emissions from industries. This article presents an economic-statistical design of the combined Shewhart X¯ and exponentially weighted moving average (EWMA) scheme (X¯&EWMA scheme) for monitoring carbon emissions from industries to allow prompt action for controlling excessive emissions. The parameters of the proposed SPM scheme have been optimized for minimizing the expected total cost, including cost from carbon emissions and operational costs of the SPM scheme. The design of the X¯&EWMA scheme has been optimized considering a wide range of shifts in the mean of the emission process, and ensuring that the constraints on inspection rate, sample size, and false alarm rate are all satisfied. Comparative studies showed that the optimal X¯&EWMA scheme reduced the expected total cost by about 40%, 77%, and 28% compared with the basic X¯, EWMA, and X¯&EWMA schemes, respectively. The impact of the design parameters on the effectiveness of the proposed SPM scheme has also been investigated by sensitivity analysis. Finally, the application of the proposed SPM scheme is demonstrated by using real data for carbon emissions from different industrial facilities. This study is expected to considerably reduce the cost owing to excessive carbon emissions from industries and widen the literature on the utilization of SPM tools in managing the quality of the environment.

Effective monitoring of carbon emissions from industrial sector using statistical process control