Chemical input/output

M.B. Hocking, A Chemical Input-Output Analysis of Municipal Solid Waste Incineration. /. Environ. Syst. 5(3), 163 (1975). 

As was said in the introduction (Section 2.1), chemical structures are the universal and the most natural language of chemists, but not for computers. Computers woi k with bits packed into words or bytes, and they perceive neither atoms noi bonds. On the other hand, human beings do not cope with bits very well. Instead of thinking in terms of 0 and 1, chemists try to build models of the world of molecules. The models ai e conceptually quite simple 2D plots of molecular sti uctures or projections of 3D structures onto a plane. The problem is how to transfer these models to computers and how to make computers understand them. This communication must somehow be handled by widely understood input and output processes. The chemists way of thinking about structures must be translated into computers internal, machine representation through one or more intermediate steps or representations (sec figure 2-23, The input/output processes defined... 

Another example of deahng with molecular structure input/output can be found in the early 1980s in Boehiinger Ingelheim. Their CBF (Chemical and Biology Facts) system [44] contained a special microprocessormolecular structures. Moreover, their IBM-type printer chain unit had been equipped with special chemical characters and it was able to print chemical formulas. 

Those based on strictly empirical descriptions Mathematical models based on physical and chemical laws (e.g., mass and energy balances, thermodynamics, chemical reaction kinefics) are frequently employed in optimization apphcations. These models are conceptually attractive because a gener model for any system size can be developed before the system is constructed. On the other hand, an empirical model can be devised that simply correlates input-output data without any physiochemical analysis of the process. For... 

The time that a molecule spends in a reactive system will affect its probability of reacting and the measurement, interpretation, and modeling of residence time distributions are important aspects of chemical reaction engineering. Part of the inspiration for residence time theory came from the black box analysis techniques used by electrical engineers to study circuits. These are stimulus-response or input-output methods where a system is disturbed and its response to the disturbance is measured. The measured response, when properly interpreted, is used to predict the response of the system to other inputs. For residence time measurements, an inert tracer is injected at the inlet to the reactor, and the tracer concentration is measured at the outlet. The injection is carried out in a standardized way to allow easy interpretation of the results, which can then be used to make predictions. Predictions include the dynamic response of the system to arbitrary tracer inputs. More important, however, are the predictions of the steady-state yield of reactions in continuous-flow systems. All this can be done without opening the black box. 

Many real problems do not satisfy these convexity assumptions. In chemical engineering applications, equality constraints often consist of input-output relations of process units that are often nonlinear. Convexity of the feasible region can only be guaranteed if these constraints are all linear. Also, it is often difficult to tell if an inequality constraint or objective function is convex or not. Hence it is often uncertain if a point satisfying the KTC is a local or global optimum, or even a saddle point. For problems with a few variables we can sometimes find all KTC solutions analytically and pick the one with the best objective function value. Otherwise, most numerical algorithms terminate when the KTC are satisfied to within some tolerance. The user usually specifies two separate tolerances a feasibility tolerance Sjr and an optimality tolerance s0. A point x is feasible to within if... 

This problem, taken from Floudas (1995), involves the manufacture of a chemical C in process 1 that uses raw material B (see Figure E9.3a). B can either be purchased or manufactured via two processes, 2 or 3, both of which use chemical A as a raw material. Data and specifications for this example problem, involving several nonlinear input-output relations (mass balances), are shown in Table E9.3A. We want to determine which processes to use and their production levels in order to maximize profit. The processes represent design alternatives that have not yet been built. Their fixed costs include amortized design and construction costs over their anticipated lifetime, which are incurred only if the process is used. 

Concept A set of chemical inputs can generate a particular light output from a light-powered molecular-level device. Such output patterns correspond to various members of the logic vocabulary. Besides offering... 

The higher prices of cars, which is balanced by subsidies, has two impacts in ASTRA first, car manufacturers increase their revenues and output, compared with BAU, and second, a few other sectors that manufacture significant shares of the fuel cell also benefit. HyWays estimates that about one third of a car s price is related to the drive train. For hydrogen-fuel-cell cars, out of this one third about 30% is assumed to be provided by the chemical sector and 40% by the electronics sector in ASTRA. The remaining 30% is still manufactured by the vehicle sector. Hence, the according shares of demand for H2 fuel-cell vehicles are shifted from the vehicles sector, which before produced 100% of the drive train, to the chemicals and electronics sectors, respectively. This affects the sectoral final demand and the input-output table calculations in ASTRA. 

Constraints (5.13) and (5.14) represent the material balance that governs the operation of the petrochemical system. The variable x 1 represents the annual level of production of process m Mpa where ttcpm is the input-output coefficient matrix of material cp in process m Mpel. The petrochemical network receives its feed from potentially three main sources. These are, (i) refinery intermediate streams of an intermediate product cir RPI, (ii) refinery final products Ff ri of a final product cfr RPF, and (iii) non-refinery streams Fn px of a chemical cp NRF. For a given subset of chemicals cp CP, the proposed model selects the feed types, quantity and network configuration based on the final chemical and petrochemical lower and upper product demand Dpet and DPet for each cp CFP, respectively. In constraint (5.15), defining a binary variable yproc et for each process m Mpet is required for the process selection requirement as yproc et will equal 1 only if process m is selected or zero otherwise. Furthermore, if only process m is selected, its production level must be at least equal to the process minimum economic capacity B m for each m Mpet, where Ku is a valid upper... 

Because we can measure—or reliably estimate—all three of these brain functions, we can construct a three-dimensional model representing (1) the energy level of the brain and its component parts (Factor A, for Activation) (2) the input-output gating status of the brain, including its internal signaling systems (Factor I, for Information Source) and (3) the modulatory status of the brain, which is determined by those chemical systems that determine the mode of processing to which the information is subjected (Factor M, for Modulation). 

W. M. Meylan and P. H. Howard, Chemical Market Input/ Output Analysis of Selected Chemical Substances to Assess Sources of Environmental Contamination ... 

Such equations allow calculations to be carried out to quantify the materials used and produced during the course of a fermentation in the same manner as for a chemical reaction process. If the fermentation scheme is simplified to the situation shown in Fig. 5.40, then an input-output table can be drawn up for the streams shown, given the composition of, say, the carbon and energy feed stream and the gaseous product stream. 

The construction of a mass balance model follows the general outline of this chapter. First, one defines the spatial and temporal scales to be considered and establishes the environmental compartments or control volumes. Second, the source emissions are identified and quantified. Third, the mathematical expressions for advective and diffusive transport processes are written. And last, chemical transformation processes are quantified. This model-building process is illustrated in Figure 27.4. In this example we simply equate the change in chemical inventory (total mass in the system) with the difference between chemical inputs and outputs to the system. The inputs could include numerous point and nonpoint sources or could be a single estimate of total chemical load to the system. The outputs include all of the loss mechanisms transport... 

Fig. 1 Design principle ofYES logic gates with luminescence output and chemical input accordingto photoinduced electron transfer (PET) concepts.

Most of the chemists will agree that chemical structure is a common denominator in the majority of chemical work and that it seems naturally to discuss the ways how chemical structures can be handled (input, output, displayed, compared, searched, ranked, etc.) by computers (ref. 1) in general and by personal computers in particular. 

By including the reactor/separation/recycle level in the hierarchical approach, plantwide control can be considered at an early stage of design. In most cases, the separation is considered as a black-box. By black-box we mean that some targets are set, for example as species recovery or product purities. The separation is then modeled based on simple input-output component balances. The decisions to be taken and the detail of the results obtained depend on the information about the chemical reactor that is available. 

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