In chemical and allied industries, process design sustainability has gained public concern in academia, industry, government agencies, and social groups. Over the past decade, a variety of sustainability indicators have been introduced, but with various challenges in application. It becomes clear that the industries need urgently practical tools for conducting systematic sustainability assessment on existing processes and/or new designs and, further, for helping derive the most desirable design decisions. This paper presents a systematic, general approach for sustainability assessment and design selection through integrating hard (quantitative) economic and environmental indicators along with soft (qualitative) indicators for social criteria into design activities. The approach contains four modules: a process simulator module, an equipment and inventory acquisition module, a sustainability assessment module, and a decision support module. The modules fully utilize and extend the capabilities of the process simulator Aspen Plus, Aspen Simulation Workbook, and a spreadsheet, where case model development, data acquisition and analysis, team contribution assessment, and decision support are effectively integrated. The efficacy of the introduced approach is illustrated by the example of biodiesel process design, where insightful sustainability analysis and persuasive decision support show its superiority over commonly practiced technoeconomy evaluation approaches.
Kertas kerja ini menerangkan mengenai kegunaan jaringan neural tiruan (ANN) untuk mengesan dan membaiki kesilapan dalam loji proses. Dalam penyelidikan ini, ANN menggunakan dua lapisan dalam strategi diagnostik hirarki. Lapisan pertama mengenal pasti nod di mana kesilapan bermula sementara lapisan kedua membahagikan kesilapan yang berlaku pada nod tertentu. Arkitek model ANN adalah berasaskan beberapa lapisan rangkaian suapan hadapan dan menggunakan algoritma luncuran belakang dalam skema latihan. Untuk mendapatkan konfigurasi ANN yang terbaik, analisis topologi dilakukan. Keberkesanan kaedah ini ditunjukkan oleh kajian kes melibatkan turus pemecahan asid lemak. Keputusan menunjukkan sistem ini berjaya mengesan kesilapan tunggal dan fana yang terdapat dalam proses tersebut.
Kata kunci: Pengenalpastian dan diagnostik kesilapan proses, strategi diagnostik hirarki, jaringan neural tiruan, turus pemecahan asid lemak
This paper focuses on the use of artificial neural network (ANN) to detect and diagnose fault in process plant. In this work, the ANN uses two layers of hierarchical diagnostic strategy. The first layer diagnoses the node where the fault originated and the second layer classifies the type of faults or malfunctions occurred on that particular node. The architecture of the ANN model is founded on a multilayer feed forward network and used back propagation algorithm as the training scheme. In order to find the most suitable configuration of ANN, a topology analysis is conducted. The effectiveness of the method is demonstrated by using a fatty acid fractionation column. Results show that the system is successful in detecting original single and transient fault introduced within the process plant model.
Key words: Process fault detection and diagnosis, hierarchical diagnostic strategy, artificial neural network, fatty acid fractionation column
Fractionation process of oleochemical products in Malaysia normally uses conventional distillation columns reduced cost and energy consumption. However, research on its feasibility for oleochemical fractionation is Aspen Plus. A step-by-step design procedure was introduced to aid the model development. Economic and environmental assessment was performed and compared with conventional distillation columns. Hydraulic analysis of several packing type was also performed to gain insights on the column hydrodynamic behaviour. 2 emissions. Overall, assessment is important to assess its feasibility especially during the early stage of technology development prior to industrial implementation.
<p>Injuries, accidents or even fatalities while working in pilot plant are reported worldwide. The implementation of process hazards analysis (PHA) in pilot plant is expected to further reduce the risks of accidents. Hazard and operability (HAZOP) analysis is one of the most widely used methods for PHA. Generally, the outcome of HAZOP analysis could results in identifying large number of hazards thus poses a challenge for assessors to take actions in dealing with all the hazards. The common practice in prioritizing the critical hazards is based on assessors’ experience through deductive judgment using rating scale, taking into consideration safety and the associated costs. However the novel operations and process used, unproven or changing technology, and lack of safety information due to developmental stages have led to poor hazards prioritization and difficulty in selecting actions. This paper presents an application of analytical hierarchy process (AHP) in prioritizing HAZOP analysis for pilot plant. Through this approach, the significant hazards identified using HAZOP will be quantitatively weighted and ranked based on their priority along with the appropriate counter measures to be taken. Application of this approach at the high pressure CO2-hydrocarbon absorption system pilot plants as case study showed that the proposed methodology is capable of identifying and ranking the significant hazards in the process following HAZOP analysis. This is particularly useful as a leading indicator to process designers/engineers/researcher in prioritizing their efforts and resources on more significant hazards, hence prevent accidents of the pilot plant.</p><p>Chemical Engineering Research Bulletin 19(2017) 87-95</p>
The design for sustainability in chemical process design should consider the three pillars of sustainability namely economic feasibility, environmental friendliness and social benefits. In reality however, agreeability among the criteria are not easily obtained because of the dependencies that exist between them. Also conflict of interest between the decision makers makes the decision making becomes more complex and therefore trade off is often needed. To encounter this complex interaction, analytic network process (ANP) is adopted utilizes its capability to account the interdependencies among level of attributes. Using ANP, a hierarchical decision network model using elements that are determinant to engineers and managers is developed that also taken into account the dependencies that exist within the framework. An example of several biodiesel process designs from literature are examined to shows the applicability of the approach. Overall, the approach offers a practical and systematic tool for aiding sustainable decision making in chemical process design specifically those that deal with complex and interacting decision environment.
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