Biomass Gasification and Applied Intelligent Retrieval in Modeling

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies

Batch process integration for resource conservation toward cleaner production – A state-of-the-art review

Process Integration (PI) for resource conservation has become an increasingly popular topic of research in line with growing emphasis toward circular economy and rising concern on environmental protection. PI for resource conservation in continuous processes has been relatively well-established as compared to PI developments for batch processes that have lagged behind and are gradually getting attention. This paper presents a comprehensive and up-to-date review on the development and future research direction of batch process integration (BPI) methodologies for resource conservation covering energy, water and other types of industrial resources. The BPI methodologies are categorized according to batch process scheduling flexibility. The first category is the fixed, or pre-specified schedule BPI methodologies whereby time is treated as a parameter. The second category is the flexible or variable-schedule BPI methodologies that considers time as a variable. The fixed and flexible-schedule batch processes are further sub-divided and analyzed as graphical and mathematical programming-based BPI tools. Covering literature from 2000 to 2021 that comprises more than 160 publications in over twenty years period on BPIRC progress, trends and direction, the review highlights the significant role of BPI in driving small to medium scale industries toward cleaner production and circular economy

Maximising heat recovery in batch processes via product streams storage and shifting

In a batch process, either direct or indirect heat integration may be employed. The former involves direct heat transfer from hot to cold process streams. In the latter, heat from a hot process stream is first transferred to an intermediate fluid where the heat is stored until it is finally transferred to a cold stream. Storage of product streams allows direct heat integration to be delayed, thereby providing an opportunity for energy conservation while avoiding the use of an intermediate fluid. This paper presents a new methodology for batch heat integration that involves the direct storage of product streams within the procedure to set the minimum utility targets. Application of the proposed methodology on illustrative examples demonstrates that significant energy reduction can be achieved by shifting product streams on the time scale. Potential reductions of 33.2% cold utility and 45.1% hot utility were estimated for the first example when the product stream was stored. Similarly, reductions of 3.5% cold utility and 6.5% hot utility were observed for a two-product batch plant when the cooling requirement for one of the products was shifted on time scale

A linear mathematical model to determine the minimum utility targets for a batch process

This paper presents a mathematical model to determine the minimum utility targets for a batch process. The model was developed based on the source-demand classification of process streams where each stream was simultaneously treated as a source at its shifted supply temperature and as a demand at its shifted target temperature. The proposed model was formulated as a linear programming model (LP) and hence, guarantees the global optimal solution. The mathematical model can be used to calculate the utility targets for any fixed-schedule batch process. As the formulation is linear solutions, it can be guaranteed to be globally optimal. Applications of the proposed mathematical formulation on two illustrative examples demonstrate significant energy saving potential. Potential reductions of 52 % in hot utility and 48 % in cold utility have been estimated for the first example. Similarly, reductions of 71 % in hot utility and 65 % in cold utility can be potentially achieved for the second example

Biomass Gasification and Applied Intelligent Retrieval in Modeling

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies