A Conceptual Efficient Design Of Energy Recovery Systems Using A New Energy-area Key Parameter

Energy integration in petrochemical and refining industries is an effective concept to minimize dependence on heating and cooling utilities through networks of exchanger equipment. Pinch Analysis is very popular and successful technique to optimize heat recovery between heat sources and sinks. Yet, design of networks of exchangers is challenging and requires careful attention to energy consumption and exchanger areas. This work presents a graphical methodology to design exchanger networks taking into account both heat loads and transfer areas of exchanger units in one single information. A new parameter is introduced for design that is the ratio between the heat load and the exchanger area and is determined in kW/m2. It is defined as an energy-area parameter expressing how much heat the exchanger would transfer per every meter square of area. Such parameter will be valuable key in design to screen matches of exchangers providing that both the heat and area are considered. The higher the value of the parameter, the better the performance of the exchanger, i.e. maximum heat transfer rate for minimum exchanger area. The design methodology embedding the energy-area parameter guarantees HEN designs with energy targets and minimum areas. A case is studied for the production of 100,000 t/y of dimethyl ether. An optimum network is generated by applying the new parameter with less exchanger areas and hot utility of 25% and 30%, respectively compared with an automated design by Aspen Energy Analyzer®. Also, substantial savings of about 47% in the total cost of the network are earned

Developing Model for Fuel Consumption Optimization in Aviation Industry

The contribution of aviation to society and economy is undisputedly significant. The aviation industry drives economic and social progress by contributing prominently to tourism, commerce and improved quality of life. Identifying the amount of fuel consumed by an aircraft while moving in both airspace and ground networks is critical to air transport economics. Aviation fuel is a major operating cost parameter of the aviation industry and at the same time it is prone to various constraints. This article aims to develop a model for fuel consumption of aviation product. The paper tailors the information for the fuel consumption optimization in terms of information development, information evaluation and information refinement. The information is evaluated and refined using statistical package R and Factor Analysis which is further validated with neural networking. The study explores three primary dimensions which are finally summarized into 23 influencing variables in contrast to 96 variables available in literature. The 23 variables explored in this study should be considered as highly influencing variables for fuel consumption which will contribute significantly towards fuel optimization. Keywords: Fuel Consumption, Civil Aviation Industry, Neural Networking, Optimizatio

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

OPTIMIZATION OF RECTIFICATION PROCESS USING MOBILE CONTROL ACTION WITH ACCOUNT FOR CRITERION OF MAXIMIZING SEPARATION QUALITY

The use of mobile control action allows the improvement of technical-economical characteristics of the rectification process and allows for operation regimes that can’t be achieved with traditional control approaches. Mobility lies in the ability to choose the movement law of compound source and energy in the spatial region of apparatus. Mobile control over the rectification process can be realized by changing the column feed point. An optimal number of feed trays must be determined with consideration of cost and output performance, and also the quality of the target product. The work aimed to develop a method for calculating optimal control action, including mobile ones, on the rectification process with additional account for the criterion of maximizing quality of target product, and also, comparison of static column profiles that are optimal by different criteria. Mathematical modeling of the rectification column for separation of water-methanol mixture revealed that increasing quality requirements to target products decreases the number of the optimal feed tray. A method was described for process optimization by the normalized criterion that accounts for separation quality and power consumption. The method was used to determine optimal values of traditional (flows of heat into the column's cube and phlegm) and mobile (feed tray number) control actions that provide the best technical-economical parameters of the rectification column. A proof is presented for the existence and uniqueness of solutions for this optimization problem and the effectiveness of using mobile actions for different requirements to target. The optimal temperature profile of the culms was studied and their characteristic features that correspond to different specific and normalized optimization criteria were foun

Multi-scale modelling and optimisation of sustainable chemical processes

This dissertation explores the process modelling and optimisation of chemical processes under sustainability criteria. Resting on process systems engineering techniques combined with life cycle assessment (LCA), we present implementation strategies to improve flowsheet performance and reduce environmental impacts from early design stages. We first address the relevance of sustainability assessments in the sector and present process and environmental modelling techniques available. Under the observation that chemical processes are subject to market, technical, and environmental fluctuations, we next present an approach to account for these uncertainties. Process optimisation is then tackled by combining surrogate modelling, objective-reduction, and multi-criteria decision analysis tools. The framework proved the enhancement of the assessments by reducing the use of computational resources and allowing the ranking of optimal alternatives based on the concept of efficiency. We finally introduce a scheme to assess sustainable performance at a multi-scale level, from catalysis development to planet implications. This approach aims to provide insights about the role of catalysis and establish priorities for process development, while also introducing absolute sustainability metrics via the concept of ‘Planetary boundaries’. Ultimately, this allows a clear view of the impact that a process incurs in the current and future status of the Earth. The capabilities of the methods developed are tested in relevant applications that address challenges in the sector to attain sustainable performance. We present how concepts like circular economy, waste valorisation, and renewable raw materials can certainly bring benefits to the industry compared to their fossil-based alternatives. However, we also show that the development of new processes and technologies is very likely to shift environmental impacts from one category to another, concluding that cross-sectorial cooperation will become essential to meet sustainability targets, such as those determined by the Sustainable Development Goals.Open Acces

A Comprehensive Techno-Economic Framework for Shale Gas Exploitation and Distribution in the United States

Over the past years, shale gas has turned into one of the most significant sources of energy in the United States. Technological advancements have provided the energy industry with the necessary tools to allow the economic exploitation of an enormous volume of natural gas trapped in shale formations. This has boosted the domestic gas production and generated a boom in other sectors of the economy in the country. However, major challenges are involved in the development of shale gas resources. A drastic decline of wells’ productivity, the costs involved in the gas production and distribution facets, and the volatile behavior of the energy market represent some of the complexities faced by a gas operator. In this context, the utilization of a comprehensive frameworks to analyze and develop long-term strategies can represent a meaningful supporting tool for shale gas operators. The main objective of this research work is the development and implementation a novel techno-economic framework for the optimal exploitation and delivery of shale gas in the United States. The proposed framework is based on an interdisciplinary approach that combines data driven techniques, petroleum engineering practices, reservoir simulations and mathematical programming methods. Data analysis algorithms are implemented to guide the decision-making processes involved in the unconventional reservoir and define the predominant trends of certain exogenous parameters of the system. Petroleum engineering practices and reservoir simulation models are required for a realistic description of the formations and the proper definition of strategies to extract the gas from the shale rock. Finally, the mathematical programming is required for describing the surface facilities design and operations to ensure the allocation of the shale gas in the different commercialization points. The output of this framework will provide the optimal operations and infrastructure by maximizing the net present value (NPV). To demonstrate the efficacy of the proposed decision-making structure, a case study based on the liquid-rich region of the Marcellus play is considered in this work. The application of the proposed framework depicts the influence of reservoir complexities and external factors in establishing optimal strategic decisions for the exploitation, processing and allocation of shale gas. The coordination of the different facets including the drilling and completion activities and the design and operation of the surface facilities has a key role in maintaining the economy of a shale gas venture above its economic threshold

Dynamic risk assessment of process operations

Process engineering systems have become increasingly complex and more vulnerable to potential accidents. The risks posed by these systems are alarming and worrisome. The operation of these complex process engineering systems requires a high level of understanding both from the operational as well as the safety perspective. This study focuses on dynamic risk assessment and management of complex process engineering systems’ operations. To reduce risk posed by process systems, there is a need to develop process accident models capable of capturing system dynamics in real-time. This thesis presents a set of predictive process accident models developed over four years. It is prepared in manuscript style and consists of nine chapters, five of which are published in peer reviewed journals. A dynamic operational risk management tool for process systems is developed, considering evolving process conditions. The obvious advantage of the developed methodologies is that it dynamically captures the real time changes occurring in the process operations. The real time risk profile provided by the methodologies developed serve as performance indicator for operational decision making. The research has made contributions on the following topics: (a) process accident model considering dependency among contributory factors, (b) dynamic safety analysis of process systems using a nonlinear and non-sequential accident model, (c) dynamic failure analysis of process systems using principal component analysis and a Bayesian network, (d) dynamic failure analysis of process systems using a neural network and (e) an integrated approach for dynamic economic risk assessment of process systems

Ethanol Production from Bioresources and Its Kinetic Modeling: Optimization Methods

Ethanol is viable alternative fuel and it’s substitute to fossil fuel has gained importance with rise in fuel prices. The chapter elaborates about methods of production from different types of bio resources like molasses, starch and cellulose commercially. The chapter also details about different methods of pretreatment for cellulisic and starchy raw materials. This also includes hydrolysis using acid and enzymes. The modes of ethanol fermentation using bioreactors like batch fed batch and continuous operation will be discussed. The growth kinetics models like monad logistic model will be elaborated. The product formation growth associated models like Leudiking piret model and parameter estimation methods will be described. Optimization of process variables using response surface methodology and media optimization using PB design will be elaborated. The application of ANN in modeling will be described