Load optimisation within the cogeneration system is crucial in enhancing energy efficiency. Instead of constructing the mathematical optimisation model or applying the commercial utility optimisation software with a licensing fee, this study proposes a holistic P-graph method to model and optimise the cogeneration system using the free and user-friendly software, P-graph Studio. To consider actual performance of unit operations, novel slope-constant element is introduced in the P-graph structure to adapt the variable output-input ratios in the form of linear performance model with non-zero constant. This overcomes the functionality of the conventional P-graph structure that only considers fixed output-input ratio. A case study of an industrial cogeneration system is optimised using the proposed P-graph method, resulting in 1.24 % reduction of operating cost and CO2 emission: equivalent to savings of RM 12,822,300/year and 4,300 tonnes CO2 emission/year. Two operating strategies are proposed to revise the optimal operating method by modifying the P-graph superstructure to ensure adequacy of the utility margin in meeting the potential maximum utility demand. The operating cost saving of 0.53 % is achieved after revision to meet both operational efficiency and reliability of the cogeneration system which results in savings of RM 5,454,900/year and 1,800 tonnes CO2 emission/year.

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Reduction of CO2 emissions from industrial facilities is of utmost importance for sustainable development. Novel process systems with the capability to remove CO2 will be useful for carbon management in the future. It is well-known that major determinants of performance in process systems are established during the design stage. Thus, it is important to employ a systematic tool for process synthesis. This work approaches the design of polygeneration plants with negative emission technologies (NETs) by means of the graph-theoretic approach known as the P-graph framework. As a case study, a polygeneration plant is synthesized for multiperiod operations. Optimal and alternative near-optimal designs in terms of profit are identified, and the influence of network structure on CO2 emissions is assessed for five scenarios. The integration of NETs is considered during synthesis to further reduce carbon footprint. For the scenario without constraint on CO2 emissions, 200 structures with profit differences up to 1.5% compared to the optimal design were generated. The best structures and some alternative designs are evaluated and compared for each case. Alternative solutions prove to have additional practical features that can make them more desirable than the nominal optimum, thus demonstrating the benefits of the analysis of near-optimal solutions in process design.

We propose a process graph (P-graph) approach to develop ecosystem networks from knowledge of the properties of the component species. Originally developed as a process engineering tool for designing industrial plants, the P-graph framework has key advantages over conventional ecological network analysis techniques based on input-output models. A P-graph is a bipartite graph consisting of two types of nodes, which we propose to represent components of an ecosystem. Compartments within ecosystems (e.g., organism species) are represented by one class of nodes, while the roles or functions that they play relative to other compartments are represented by a second class of nodes. This bipartite graph representation enables a powerful, unambiguous representation of relationships among ecosystem compartments, which can come in tangible (e.g., mass flow in predation) or intangible form (e.g., symbiosis). For example, within a P-graph, the distinct roles of bees as pollinators for some plants and as prey for some animals can be explicitly represented, which would not otherwise be possible using conventional ecological network analysis. After a discussion of the mapping of ecosystems into P-graph, we also discuss how this framework can be used to guide understanding of complex networks that exist in nature. Two component algorithms of P-graph, namely maximal structure generation (MSG) and solution structure generation (SSG), are shown to be particularly useful for ecological network analysis. These algorithms enable candidate ecosystem networks to be deduced based on current scientific knowledge on the individual ecosystem components. This method can be used to determine the (a) effects of loss of specific ecosystem compartments due to extinction, (b) potential efficacy of ecosystem reconstruction efforts, and (c) maximum sustainable exploitation of human ecosystem services by humans. We illustrate the use of P-graph for the analysis of ecosystem compartment loss using a small-scale stylized case study, and further propose a new criticality index that can be easily derived from SSG results.

Chemical process simulation has become one of the most important tools for the analysis of process networks. The simulation software currently available are not capable of automatically generating the process structure, the designer must provide it as an input for the simulation. This limits the contribution of simulation to the latter stages of design after the structure has been clearly defined. Since the P-graph methodology was originally conceived to generate process structures systematically, it can be used to produce the topology of the problem automatically based on rigorous combinatorial axioms and algorithms. In this work, the properties of two P-graph algorithms are exploited to automatically generate alternative structures in a commercial simulator, conferring the latter an improved capacity to assist during the early stage of design. Initially, the maximal structure generation (MSG) algorithm is employed to identify a rigorous superstructure from the initial set of plausible operating units. The solution structure generation (SSG) algorithm is then used to enumerate all combinatorially feasible processes included in the superstructure. Each process structure is individually exported to Aspen Plus®, where rigorous models are used to simulate its performance. A set of alternative processes ranked by their economic performance can be generated. This integrated methodology is employed in a case study for producing methyl lactate from methanol and lactic acid. This work demonstrates that integration of P-graph with rigorous simulation constitutes an enhanced tool for process synthesis that automates the generation of process alternatives, providing useful information and additional insight of the synthesis problem.