Artificial Intelligence Assisted Dynamic Control of Environmental Emissions From Hybrid Energy Process Plants (HEPP)

Abstract

A model digital data processing platform is proposed based on "deep-learning" methodology that can be used to identify the emissions patterns from process plants with hybrid energy recovery and energy generation facilities. The non-invasive dynamic monitoring and correlation of particulate, VOCs and other greenhouse gas emissions from semi-batch and continuous process plants is demonstrated with use of neural encoding and pattern recognition using a multi-layer perceptron and multi-stack encoder configuration. A multi-layer environmental perceptron (MLEP) is developed based on the above analyses that aims to detect patterns of emission types, rates and concentrations as a function of variation of plant operational conditions and process variables. Four different task algorithms are constructed and are currently trained for use in (i) In-Plant Product Quality Control Domain and (ii) In-Plant Process Efficiency Target Control Domain. As a further consequence, environmental impact assessment is considered within the hazards and process safety frameworks that conventionally issue sanctions and penalize non-compliance with imposition of environmental levy scales rather than offering process improvement incentives. The latter is demonstrated to be possible by facilitating dynamic corrective action and hazard prevention using MLEP platforms should emission ceilings be frequently and/or periodically exceeded in 24/7 continuous plant operations. Potential applications of the MLEP (MLEP) are illustrated in the context of dynamic emissions control and abatement in hybrid energy process plants (HEPP) and combined power plants using process-integrated CO 2 capture and storage schemes.

Integration of Digital Economy and Circular Economy: Current Status and Future Directions

“…The main application areas are the consumption and use of digital sharing platforms [23,52,68,74,76], the transportation and packaging of food [100] and building prefabricated materials [79], and the repair and maintenance of the telecommunications industry [112]. [51,58,72], reuse/remanufacture of consumer goods [113], automotive charging and energy storage systems [114], parts [83,115] and digital recorder [116], disposal of production and domestic wastes [117], disassembly/recycling and disposal of mixed energy and factory gases [118], metal parts [119], e-waste [120], and disassembly/recycling and reuse/remanufacturing of waste polymers [54,57,98], industrial wastes [121], and textile materials [94]. In addition, applications throughout the Whole Life Cycle Stage are mainly associated with products, including smart PSS [89,122], recycling product management [85], and product life extension [70], followed by the construction industry, mainly in terms of computer simulation of the full life cycle stage [78] and life cycle information conversion and exchange [123], and the garment industry from textiles to textile recycling [95].…”