Treatment technologies for cooling water blowdown: A critical review

Cooling water blowdown (CWBD) generated from different industries and district cooling facilities contains high concentrations of various chemicals (e.g., scale and corrosion inhibitors) and pollutants. These contaminants in CWBD streams deem them unsuitable for discharge into surface water and some wastewater treatment plants. The pollutants present in CWBD, their sources, and the corresponding impacts on the ecosystem are discussed. The international and regional (Gulf states) policies and regulations related to contaminated water discharge standards into water bodies are examined. This paper presents a comprehensive review of the existing and emerging water treatment technologies for the treatment of CWBD. The study presents a comparison between the membrane (membrane distillation (MD), reverse osmosis (RO), nanofiltration (NF), and vibratory shear enhanced membrane process (VSEP)) and nonmembrane-based (electrocoagulation (EC), ballasted sand flocculation (BSF), and electrodialysis (ED)) technologies on the basis of performance, cost, and limitations, along with other factors. Results from the literature revealed that EC and VSEP technologies generate high treatment performance (EC~99.54% reduction in terms of silica ions) compared to other processes (membrane UF with reduction of 65% of colloidal silica). However, the high energy demand of these processes (EC~0.18-3.05 kWh/m3 and VSEP~2.1 kWh/m3) limit their large-scale applications unless connected with renewable sources of energy.Funding: This research is funded by Ministry of Municipality and Environment in Qatar, Project MME contract no. P2020/1.Scopu

Environmental sustainability through process integration, control and optimization

[No abstract available]Scopu

Experimental study of optimizing control of continuous chromatographic separation process

This thesis introduced an improved single-column chromatographic (ISCC) separation process with the final objective to make this process distinct from existing single-column chromatographic separation processes by physical modifications and conceptual advances. The performance of this ISCC process was evaluated by experimental implementation to separate a mixture of guaifenesin enantiomers. In ISCC process, different standard HPLC peripherals were used as building blocks and some standard parts of the commercial HPLC system were redesigned to overcome the existing limitations for better performance. Fraction collection schemes and mechanism are the important features of this improvement. This fraction collection system allows accommodation of overlapped peaks from adjacent cycles and reduce the overall time delay of the process. These process design modification provide a wider degree of freedom: injection volume, cycle time, desorbent flow rate, feed concentration and fraction collection intervals. A robust online monitoring system was designed which was relatively inexpensive and was able to offer high frequency and accurate analysis of the samples compared to other devices. The proposed ISCC process was assembled in a laboratory and commissioned successfully. Process performance was optimized by a multi-objective stochastic optimization technique based on genetic algorithm (GA). The optimization problem was appropriately formulated with the aim of maximization of productivity and minimization of desorbent requirement. Performance of the ISCC process was also compared with a similar SMB process. This study provided the basis for reaping the full potential benefits of a single column process that adopts cyclic injection. Besides, relative contribution of the decision variables were ascertained through the study of their effects on the performance indicators. Detector calibration and determination of adsorption isotherm parameters were done simultaneously by adopting a new method named nonlinear direct inverse method, which is relatively fast, and economical technique compared to existing alternatives. A `cycle to cycle’ model predictive control (MPC) scheme was developed in-house to guarantee product and process specifications for obtaining optimized profitability. The performance of this MPC scheme was demonstrated through simulation studies. Finally, the cycle-to-cycle optimizing controller developed for the ISCC process for the separation of a mixture of guaifenesin enantiomers. Key implementation issues were accuracy of the online measurement system and integration and automation of the ISCC process with online measurement system and controller. This was achieved by designing and developing a human machine interface (HMI) that was able to effectively communicate among the three essential components of the control loop. The performance of the controller was tested for set point tracking and disturbance rejection. Results indicate that the designed ISCC process with the online monitoring system was able to run at the optimal operating conditions and deliver the product requirements as confirmed by open-loop and close-loop experiments.DOCTOR OF PHILOSOPHY (SCBE

Environmental sustainability through process integration, control and optimization

[No abstract available]Scopu

Process safety and abnormal situation management

This review paper summarizes process safety aspects of abnormal situations and incidents in industrial facilities. Major accidents and their negative impacts on health, safety and environment have shown the importance of enhancing safety culture, understanding causes of abnormal situations and determine effective management strategies. Flaring is a safety industry practice to control and manage processes during normal and abnormal operations. This paper identifies current efforts made by industry and researchers toward better management of abnormal situation for flare reductions. These efforts came about because of current and impending environmental legislation. The paper finds that future strategies developed for ASM must consider safety as well as environmental and economic implications; and it highlights the challenges and future guidelines for safer abnormal situation management.This paper was made possible by NPRP grant No 6-678-2-280 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the author[s].Scopu

Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network

This research aims to enhance the safety level and crash resiliency of targeted woven roving glass/epoxy composite material for various industry 4.0 applications. Advanced machine learning algorithms are used in this study to figure out the complicated relationship between the crashworthiness parameters of the hexagonal composite ring specimens under lateral compressive, energy absorption, and failure modes. These algorithms include random forest (RF) classification and artificial neural networks (ANN). The ultimate target is to develop a robust multi-modal machine learning method to predict the optimum geometry (i.e., hexagonal ring angle) and suitable in-plane crushing arrangements of the hexagonal ring system for targeted crashworthiness parameters. The results demonstrate that the suggested RF-ANN-based technique can predict the optimal composite design with high accuracy (precision, recall, and f1-score for test and train dataset were 1). Furthermore, the confusion matrix validates the random forest classification model's accuracy. At the same time, the mean square error value serves as the loss function for the ANN model (i.e., the loss function values were 2.84 × 10−7 and 6.40 × 10−7, respectively, for X1 and X2 loading conditions at 45° angle). Furthermore, the developed models can predict crashworthiness parameters for any hexagonal ring angle within the range of the trained dataset, requiring no additional experimental effort

Inherently safer design tool (i-SDT): A property-based risk quantification metric for inherently safer design during the early stage of process synthesis

In this work, an Inherently Safer Design Tool (i-SDT) is presented for early stage process synthesis to characterize and track the risk associated with different life-cycle phases of industrial processes. It also helps to develop characteristic equations for different safety parameters (i.e., flammability, explosiveness, toxicity, etc.) under various operating conditions. This property-based inherent safety quantification metric is a tailor made semi-quantitative safety analysis tool which provides safety assessment in a continuous manner to overcome the subjective nature of the existing available safety metrics. The core of this design and safety assessment tool is probabilistic risk quantification using accident and incident investigation (with over 600 incidents and within 27 years of time span), a property integration model and an exponential curve fitting method. The proposed safety metric has the flexibility to operate by identifying the major accident-prone units/sections of a process, as well as the major safety and operating parameters. The final output of this i-SDT tool is a cluster safety parameter score (CSP) which provides insights regarding the investigated unit/section or process for carrying out inherent safer design using a very limited amount of process information. The developed i-SDT tool was applied to compare different technologies of Ammonia processes in order to assess the safer option in terms of risks associated with the accident-prone unit/section and to highlight the areas of safety improvement in any existing process using the inherent safer design principles. In the future, this metric can can be embedded into a techno-economic framework to perform the cost and safety analysis simultaneously using available materials, design and accident information. - 2018 Elsevier LtdThis paper was made possible by NPRP grant No 6-678-2-280 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the author[s]. The author thanks Ahmed AlNouss for his contribution in developing the necessary simulation models for the presented Ammonia case study.Scopu

Managing Uncertain Industrial Flares during Abnormal Process Operations using an Integrated Optimization and Monte Carlo Simulation Approach

In this work, an integrated optimization framework with Monte Carlo (MC) simulation techniques is suggested for the systematic synthesis of energy alternative tools, such as cogeneration (COGEN) systems, which can effectively manage industrial flares with uncertain occurrence patterns. The optimization model that was previously developed is now extended to incorporate the risk associated with the uncertain nature of the flaring events that are probabilistically characterized based on empirically meaningful historical samples. The model aims at minimizing the total annualized cost including fixed and operating costs of the system, the value of by- and co-products (i.e., power, excess heat), and regulatory taxes/credits associated with Green House Gases (GHGs). A base case ethylene production plant is presented to illustrate the applicability of the proposed approach and highlight trade-offs between different performance objectives (economic, environmental and energy-related). The results show that some of the examined factors (i.e., CO2 tax savings) can be severely affected by small variations in flaring profiles, whereas others are only slightly affected by such variability (i.e., power vs. heat generation curves, fixed and operating costs). Therefore, the uncertain nature of flaring events may be of high importance in process performance and should be inevitably considered during abnormal situation management. 1 2017 Elsevier B.V.Scopu

Optimization of an improved single-column chromatographic process for the separation of enantiomers

This work addresses optimization of an improved single-column chromatographic (ISCC) process for the separation of guaifenesin enantiomers. Conventional feed injection and fraction collection systems have been replaced with customized components facilitating simultaneous separation and online monitoring with the ultimate objective of application of an optimizing controller. Injection volume, cycle time, desorbent flow rate, feed concentration, and three cut intervals are considered as decision variables. A multi-objective optimization technique based on genetic algorithm (GA) is adopted to achieve maximum productivity and minimum desorbent requirement in the region constrained by product specifications and hardware limitations. The optimization results along with the contribution of decision variables are discussed using Pareto fronts that identify non-dominated solutions. Optimization results of a similar simulated moving bed process have also been included to facilitate comparison with a continuous chromatographic process

Integrated Data (i-Data), Mining and Utilization Approach for Effective Flare Management Strategies

Upset emissions occur during plant startup, shutdown, maintenance, malfunction, and flaring incidents. A wide range of these upsets cannot be managed by standalone control systems; plant personnel intervention is necessary sometimes. The methods needed to assist plant personnel to control and prevent abnormal process operations are gathered under abnormal situation management. Abnormal operations that lead to flare have significant economic, environmental, and safety impacts. Flaring is necessary for managing process upsets, however, it leads to the emission of greenhouse gases (GHG) and volatile organic compounds (VOCs), causing negative social impacts and local transient air pollution. In addition, excessive flaring results in energy and raw material losses. These are valuable commodities that must be sustained. Therefore, flare minimization during normal and abnormal operational situations has great environmental, industrial, and societal benefits. It is not possible to quantify the impacts without understanding the properties and magnitude of these upsets. Such analysis requires extensive amount of historical data. There are large sets of design, operational, and flaring data readily available; however, the challenge when it comes to flare mitigation is in using them effectively and in a timely manner. In this Article, a systematic approach to collect, analyze and utilize historical flaring data based on current industrial practices is presented. An ethylene base case study along with its historical process and flaring incidents data is used to demonstrate the significance of using and integrating data within developed flare management strategies. In the presented case, design and historical process data are used to assess the environmental impacts of abnormal incidents and to identify underlying causes and indicators that lead to process upset, that is, abnormal situations. The data sets are utilized within an optimization algorithm to identify design alternatives to mitigate process incidents and reduce its root causes. The paper highlights the challenges that are faced by environmental agencies in terms of data utilization and documentation. (Figure Presented).This paper was made possible by NPRP grant No 5-351-2-136 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu